Show of Hands:Votes: Party classification:: Q109244NA_AllX
bdanalytics
Date: (Mon) Jun 13, 2016
Introduction:
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
debugSource("~/Dropbox/datascience/R/mydsutils.R") else
source("~/Dropbox/datascience/R/mydsutils.R") ## Loading required package: caret## Loading required package: lattice# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- #NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
# '(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "No"))' # No
'(glbObsAll[, "Q109244"] != "")' # NA
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
#
# chk ref value against frequencies vs. alpha sort order
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
# glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q115611.fctr" # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012"
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024" # Done
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
# retVal <- rep_len(0, length(raw))
stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0)
# msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
# msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
# msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
# msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
# msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
# msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
# msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
# msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
retVal <- sapply(raw, function(age) {
if (is.na(age)) return(0) else
if ((age > 15) && (age <= 20)) return(age - 15) else
if ((age > 20) && (age <= 25)) return(age - 20) else
if ((age > 25) && (age <= 30)) return(age - 25) else
if ((age > 30) && (age <= 35)) return(age - 30) else
if ((age > 35) && (age <= 40)) return(age - 35) else
if ((age > 40) && (age <= 50)) return(age - 40) else
if ((age > 50) && (age <= 65)) return(age - 50) else
if ((age > 65) && (age <= 90)) return(age - 65)
})
return(retVal)
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(raw1) {
raw <- raw1
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
glbFeatsDerive[["Hhold.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)
raw <- gsub("Married (no kids)" , "MKn", raw, fixed = TRUE)
raw <- gsub("Married (w/kids)" , "MKy", raw, fixed = TRUE)
raw <- gsub("Single (no kids)" , "SKn", raw, fixed = TRUE)
raw <- gsub("Single (w/kids)" , "SKy", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("HouseholdStatus"))
glbFeatsDerive[["Edn.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Current K-12" , "K12", raw, fixed = TRUE)
raw <- gsub("High School Diploma" , "HSD", raw, fixed = TRUE)
raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
raw <- gsub("Associate's Degree" , "Ast", raw, fixed = TRUE)
raw <- gsub("Bachelor's Degree" , "Bcr", raw, fixed = TRUE)
raw <- gsub("Master's Degree" , "Msr", raw, fixed = TRUE)
raw <- gsub("Doctoral Degree" , "PhD", raw, fixed = TRUE)
return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
ordered = TRUE))
}
, args = c("EducationLevel"))
# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))
glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
mapfn = function(raw1) {
raw1[raw1 %in% ""] <- "NA"
rawVal <- unique(raw1)
if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
raw1 <- gsub("Idealist" , "Id", raw1, fixed = TRUE)
raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
}
return(relevel(as.factor(raw1), ref = "NA"))
}
, args = c(qsn))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr",
# # "Hhold.fctr",
# "Edn.fctr",
# paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[",
# toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
# "]\\.[PT]\\."),
# names(glbObsAll), value = TRUE)
glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
# glbRFESizes[["RFE.X"]] <- c(2, 3, 4, 5, 6, 7, 8, 16, 32, 64, 128, 247) # accuracy(5) = 0.6154
# glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164
glbRFEResults <- NULL
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
# RFE = "Recursive Feature Elimination"
# Csm = CuStoM
# NOr = No OutlieRs
# Inc = INteraCt
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet")
} else {
# glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
glbMdlFamilies[["All.X"]] <- c("glmnet")
# glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
# glbMdlFamilies[["RFE.X"]] <- c("glmnet")
# glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
# # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
# # , "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
# , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
# , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
# , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
# , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
# ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
# ))
}
# glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
# glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
# AllX__rcv_glmnetTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(parameter = "lambda", vals = "0.05 0.06367626 0.07 0.08 0.09167068")
# ) # max.Accuracy.OOB = 0.6020202 @ 0.55 0.03
# glbMdlTuneParams <- rbind(glbMdlTuneParams
# ,cbind(data.frame(mdlId = "All.X##rcv#glmnet"), AllX__rcv_glmnetTuneParams)
# )
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# bagEarthTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "degree", vals = "1")
# ,data.frame(parameter = "nprune", vals = "256")
# )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "Final.RFE.X.Inc##rcv#bagEarth"),
# bagEarthTuneParams))
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
pkgPreprocMethods <-
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
# Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
c(NULL
,"zv", "nzv"
,"BoxCox", "YeoJohnson", "expoTrans"
,"center", "scale", "center.scale", "range"
,"knnImpute", "bagImpute", "medianImpute"
,"zv.pca", "ica", "spatialSign"
,"conditionalX")
glbMdlPreprocMethods <- list(NULL# NULL # : default
# ,"All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# # c(NULL)))
# c("zv.pca.spatialSign")))
# ,"RFE.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# c(NULL)))
# # c("zv.pca.spatialSign")))
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
# "nzv.pca.spatialSign"))
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
"max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
"min.elapsedtime.everything",
# "min.aic.fit",
"max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- NULL # NULL : default #"auto"
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
glbMdlEnsembleSampleMethods <- c("boot", "boot632", "cv", "repeatedcv"
# , "LOOCV" # tuneLength * nrow(fitDF) # way too many models
, "LGOCV"
, "adaptive_cv" # crashed for Q109244No
# , "adaptive_boot" #error: adaptive$min should be less than 3
# , "adaptive_LGOCV" #error: adaptive$min should be less than 3
)
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSelId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
# require(tidyr)
# obsOutFinDf <- obsOutFinDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutFinDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutFinDf) {
# }
)
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
# txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- # NULL #: default
c("Q109244No_AllXpreProc_cnk03_rest_out_fin.csv")
# c("Votes_Ensemble_cnk06_out_fin.csv")
glbOut <- list(pfx = "Q109244NA_AllX_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- NULL # NULL # default: script will load envir from previous chunk
glbChunks[["last" ]] <- NULL # default: script will save envir at end of this chunk
glbChunks[["inpFilePathName"]] <- NULL #"data/Q109244No_AllXNOr_cnk01_fit.models_1_fit.models_1.RData" # NULL: default or "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL,
ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 5.75 NA NAStep 1.0: import data
chunk option: eval=
## [1] "Reading file ./data/train2016.csv..."
## [1] "dimensions of data in ./data/train2016.csv: 5,568 rows x 108 cols"
## USER_ID YOB Gender Income HouseholdStatus
## 1 1 1938 Male Married (w/kids)
## 2 4 1970 Female over $150,000 Domestic Partners (w/kids)
## 3 5 1997 Male $75,000 - $100,000 Single (no kids)
## 4 8 1983 Male $100,001 - $150,000 Married (w/kids)
## 5 9 1984 Female $50,000 - $74,999 Married (w/kids)
## 6 10 1997 Female over $150,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621 Q122769
## 1 Democrat No No No No
## 2 Bachelor's Degree Democrat Yes No No No
## 3 High School Diploma Republican Yes Yes No
## 4 Bachelor's Degree Democrat No Yes No Yes No
## 5 High School Diploma Republican No Yes No No No
## 6 Current K-12 Democrat No
## Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1 Yes Public No Yes No No No Yes
## 2 Yes Public No Yes No Yes No No Yes
## 3 Yes Private No No No Yes No No Yes
## 4 No Public No Yes No Yes No No Yes
## 5 Yes Public No Yes No Yes Yes No Yes
## 6 Yes Public No No No Yes No Yes Yes
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1 Try first No No Yes Yes
## 2 Science Study first Yes Yes No No Receiving No
## 3 Science Study first Yes No Yes Receiving No
## 4 Science Try first No Yes Yes No Giving Yes
## 5 Art Try first Yes No No No Giving No
## 6 Science Try first Yes Yes No Yes Receiving No
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797
## 1 Yes Idealist No No Yes
## 2 No Pragmatist No No Cool headed Standard hours No
## 3 Yes Pragmatist No Yes Cool headed Odd hours No
## 4 No Idealist No No Cool headed Standard hours No
## 5 No Idealist Yes Yes Hot headed Standard hours No
## 6 No Pragmatist No No Standard hours
## Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1 Happy Yes Yes No No P.M. Yes Start Yes
## 2 Happy Yes Yes Yes No A.M. No End Yes
## 3 Right Yes No No Yes A.M. Yes Start Yes
## 4 Happy Yes Yes No No A.M. Yes Start Yes
## 5 Happy Yes Yes No Yes P.M. No End No
## 6
## Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1 No Circumstances Yes Yes Yes Yes No
## 2 No Me Yes Yes No Yes No Mysterious
## 3 Yes Circumstances No Yes No Yes Yes Mysterious
## 4 No Circumstances Yes No No Yes No TMI
## 5 No Me No Yes Yes Yes Yes TMI
## 6
## Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1 Yes Yes Talk Technology No No Yes
## 2 No No
## 3 No No Tunes Technology Yes Yes Yes Yes
## 4 No No Talk People No Yes Yes Yes
## 5 Yes No Tunes People No No Yes No
## 6
## Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1 No Demanding No No Cautious No Yes!
## 2 Mac Yes Cautious No Umm...
## 3 No Supportive No PC No Cautious No Umm...
## 4 Yes Supportive No Mac Yes Risk-friendly No Umm...
## 5 No Demanding Yes PC Yes Cautious No Yes!
## 6 Yes Supportive No PC
## Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1 No Space No In-person Yes No Yes
## 2 No Space Yes In-person No Yes Yes No
## 3 No Space No In-person No No Yes Yes
## 4 No Socialize Yes Online No Yes No Yes
## 5 No Socialize No Online No No Yes Yes
## 6 In-person No No Yes Yes
## Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1 Yay people! Yes No Yes Yes No Yes
## 2 Yay people! Yes Yes Yes Yes Yes No Yes
## 3 Grrr people Yes No No No No No No
## 4 Grrr people No No Yes Yes No Yes Yes
## 5 Yay people! Yes No Yes Yes Yes Yes No
## 6 Grrr people Yes No Yes Yes No No Yes
## Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1 No No No Yes No Own Optimist
## 2
## 3 Yes No No Yes No Own Pessimist Mom
## 4 No No No Yes Yes Own Optimist Mom
## 5 No No Yes No No Own Optimist Mom
## 6 Yes Yes No Yes
## Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1 Yes Yes No No Nope Yes No No
## 2 No
## 3 No No No No Nope Yes No No No
## 4 No No No Yes Check! No No No Yes
## 5 No Yes Yes Yes Nope Yes No No Yes
## 6
## Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1 No Only-child No No Yes
## 2 No No Only-child Yes No No
## 3 Yes No Yes No Yes No
## 4 Yes No Yes No No Yes
## 5 No No Yes No No Yes
## 6
## USER_ID YOB Gender Income HouseholdStatus
## 193 245 1964 Male over $150,000 Married (w/kids)
## 848 1046 1953 Male $100,001 - $150,000 Domestic Partners (no kids)
## 2836 3530 1995 Male Single (no kids)
## 4052 5050 1945 Female $75,000 - $100,000 Married (w/kids)
## 4093 5107 1980 Female $100,001 - $150,000 Married (w/kids)
## 5509 6888 1998 Female under $25,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621
## 193 Bachelor's Degree Republican Yes Yes No Yes
## 848 Democrat
## 2836 Current Undergraduate Democrat Yes Yes Yes No
## 4052 Bachelor's Degree Republican
## 4093 Bachelor's Degree Democrat No No
## 5509 Current K-12 Republican
## Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 193 No Yes Public No Yes No Yes No
## 848
## 2836 Yes Public Yes No No Yes Yes
## 4052 No Public
## 4093 No No Private No
## 5509 Yes Yes
## Q120379 Q120650 Q120472 Q120194 Q120012 Q120014 Q119334 Q119851
## 193 No Yes Science Try first Yes Yes Yes No
## 848
## 2836 Yes Yes Art Study first No Yes Yes
## 4052
## 4093 Yes
## 5509 Yes No Art Study first Yes No Yes No
## Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186
## 193 Giving Yes No Idealist Yes Yes Hot headed
## 848
## 2836 Yes Yes Idealist Yes No Cool headed
## 4052 No No No
## 4093 No No Pragmatist No Yes
## 5509 Giving No
## Q117193 Q116797 Q116881 Q116953 Q116601 Q116441 Q116448
## 193 Standard hours No Happy Yes Yes No No
## 848
## 2836 Odd hours No Happy Yes Yes No
## 4052
## 4093
## 5509
## Q116197 Q115602 Q115777 Q115610 Q115611 Q115899 Q115390 Q114961
## 193 A.M. Yes End Yes Yes Me No No
## 848
## 2836 Yes End Yes No Circumstances Yes No
## 4052 P.M. Yes Start Yes No No
## 4093 P.M. Yes Start Yes No Circumstances
## 5509
## Q114748 Q115195 Q114517 Q114386 Q113992 Q114152 Q113583 Q113584
## 193 Yes No Yes TMI No Yes Tunes Technology
## 848
## 2836 Yes No No Mysterious No Yes Tunes People
## 4052 No Yes
## 4093 Tunes People
## 5509
## Q113181 Q112478 Q112512 Q112270 Q111848 Q111580 Q111220 Q110740
## 193 No Yes Yes Yes Supportive No Mac
## 848
## 2836 Yes Yes Yes No Yes Demanding Yes PC
## 4052
## 4093 Yes Supportive
## 5509
## Q109367 Q108950 Q109244 Q108855 Q108617 Q108856 Q108754
## 193 No Cautious No Yes! No Socialize No
## 848 Yes Risk-friendly Yes Yes! No Space No
## 2836 Yes Cautious Yes Yes
## 4052
## 4093 No Risk-friendly No Yes! No Space No
## 5509
## Q108342 Q108343 Q107869 Q107491 Q106993 Q106997 Q106272 Q106388
## 193 In-person No Yes Yes No Yay people! Yes Yes
## 848 In-person Yes
## 2836 In-person Yes Yes Yes No
## 4052 No Grrr people
## 4093 In-person Yes Yes Yes Yes Yay people! Yes Yes
## 5509
## Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906 Q102674
## 193 No Yes No No Yes No No No
## 848
## 2836 Yes No No No Yes Yes No No
## 4052 No No No No
## 4093 No No No No Yes No No Yes
## 5509
## Q102687 Q102289 Q102089 Q101162 Q101163 Q101596 Q100689 Q100680
## 193 No No Own Optimist Dad Yes Yes No
## 848
## 2836 Yes Yes Rent Optimist Dad No Yes Yes
## 4052 Yes Own No
## 4093 Yes Yes Rent No Yes
## 5509
## Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 193 Yes Check! No No No Yes Yes No Yes
## 848
## 2836 Yes Check! No No No Yes Yes Yes
## 4052
## 4093 No Nope Yes No Yes Yes Yes No Yes
## 5509
## Q98078 Q98197 Q96024
## 193 No Yes Yes
## 848 No
## 2836 Yes Yes No
## 4052
## 4093 Yes Yes No
## 5509
## USER_ID YOB Gender Income HouseholdStatus
## 5563 6955 1966 Male over $150,000 Married (w/kids)
## 5564 6956 NA Male
## 5565 6957 2000 Female
## 5566 6958 1969 Male over $150,000
## 5567 6959 1986 Male $25,001 - $50,000 Married (w/kids)
## 5568 6960 1999 Male under $25,000 Single (no kids)
## EducationLevel Party Q124742 Q124122 Q123464 Q123621
## 5563 Bachelor's Degree Democrat
## 5564 Master's Degree Democrat No No
## 5565 Current K-12 Republican
## 5566 Bachelor's Degree Democrat Yes
## 5567 High School Diploma Republican
## 5568 Current K-12 Republican
## Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 5563 No Yes No Yes Yes
## 5564 No Yes Public Yes
## 5565 Public Yes
## 5566 No No No Yes Yes
## 5567 Yes Yes No
## 5568 Yes No No
## Q120379 Q120650 Q120472 Q120194 Q120012 Q120014 Q119334 Q119851
## 5563
## 5564
## 5565 Yes Yes Art Try first No Yes Yes Yes
## 5566 Yes Yes Science
## 5567 No No Science No Yes
## 5568
## Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186 Q117193
## 5563
## 5564
## 5565 Receiving
## 5566
## 5567
## 5568
## Q116797 Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q115777 Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q114517 Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q112512 Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 5563
## 5564
## 5565
## 5566
## 5567
## 5568
## 'data.frame': 5568 obs. of 20 variables:
## $ USER_ID : int 1 4 5 8 9 10 11 12 13 15 ...
## $ YOB : int 1938 1970 1997 1983 1984 1997 1983 1996 NA 1981 ...
## $ Gender : chr "Male" "Female" "Male" "Male" ...
## $ Income : chr "" "over $150,000" "$75,000 - $100,000" "$100,001 - $150,000" ...
## $ HouseholdStatus: chr "Married (w/kids)" "Domestic Partners (w/kids)" "Single (no kids)" "Married (w/kids)" ...
## $ EducationLevel : chr "" "Bachelor's Degree" "High School Diploma" "Bachelor's Degree" ...
## $ Party : chr "Democrat" "Democrat" "Republican" "Democrat" ...
## $ Q124742 : chr "No" "" "" "No" ...
## $ Q124122 : chr "" "Yes" "Yes" "Yes" ...
## $ Q123464 : chr "No" "No" "Yes" "No" ...
## $ Q123621 : chr "No" "No" "No" "Yes" ...
## $ Q122769 : chr "No" "No" "" "No" ...
## $ Q122770 : chr "Yes" "Yes" "Yes" "No" ...
## $ Q122771 : chr "Public" "Public" "Private" "Public" ...
## $ Q122120 : chr "No" "No" "No" "No" ...
## $ Q121699 : chr "Yes" "Yes" "No" "Yes" ...
## $ Q121700 : chr "No" "No" "No" "No" ...
## $ Q120978 : chr "" "Yes" "Yes" "Yes" ...
## $ Q121011 : chr "No" "No" "No" "No" ...
## $ Q120379 : chr "No" "No" "No" "No" ...
## NULL
## 'data.frame': 5568 obs. of 20 variables:
## $ Q120650: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q118117: chr "Yes" "No" "Yes" "No" ...
## $ Q118233: chr "No" "No" "No" "No" ...
## $ Q118237: chr "No" "No" "Yes" "No" ...
## $ Q116441: chr "No" "Yes" "No" "No" ...
## $ Q116197: chr "P.M." "A.M." "A.M." "A.M." ...
## $ Q115611: chr "No" "No" "Yes" "No" ...
## $ Q115899: chr "Circumstances" "Me" "Circumstances" "Circumstances" ...
## $ Q115390: chr "Yes" "Yes" "No" "Yes" ...
## $ Q114748: chr "Yes" "No" "No" "No" ...
## $ Q115195: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q113584: chr "Technology" "" "Technology" "People" ...
## $ Q112478: chr "No" "" "Yes" "Yes" ...
## $ Q112270: chr "" "" "Yes" "Yes" ...
## $ Q111848: chr "No" "" "No" "Yes" ...
## $ Q106993: chr "Yes" "No" "Yes" "Yes" ...
## $ Q106388: chr "No" "Yes" "No" "No" ...
## $ Q105655: chr "No" "No" "No" "Yes" ...
## $ Q104996: chr "Yes" "Yes" "No" "Yes" ...
## $ Q102674: chr "No" "" "No" "No" ...
## NULL
## 'data.frame': 5568 obs. of 21 variables:
## $ Q102674: chr "No" "" "No" "No" ...
## $ Q102687: chr "Yes" "" "Yes" "Yes" ...
## $ Q102289: chr "No" "" "No" "Yes" ...
## $ Q102089: chr "Own" "" "Own" "Own" ...
## $ Q101162: chr "Optimist" "" "Pessimist" "Optimist" ...
## $ Q101163: chr "" "" "Mom" "Mom" ...
## $ Q101596: chr "Yes" "" "No" "No" ...
## $ Q100689: chr "Yes" "" "No" "No" ...
## $ Q100680: chr "No" "" "No" "No" ...
## $ Q100562: chr "No" "" "No" "Yes" ...
## $ Q99982 : chr "Nope" "" "Nope" "Check!" ...
## $ Q100010: chr "Yes" "" "Yes" "No" ...
## $ Q99716 : chr "No" "" "No" "No" ...
## $ Q99581 : chr "No" "" "No" "No" ...
## $ Q99480 : chr "" "No" "No" "Yes" ...
## $ Q98869 : chr "No" "No" "Yes" "Yes" ...
## $ Q98578 : chr "" "No" "No" "No" ...
## $ Q98059 : chr "Only-child" "Only-child" "Yes" "Yes" ...
## $ Q98078 : chr "No" "Yes" "No" "No" ...
## $ Q98197 : chr "No" "No" "Yes" "No" ...
## $ Q96024 : chr "Yes" "No" "No" "Yes" ...
## NULL## Warning in myprint_str_df(obsDf): [list output truncated]## [1] "Reading file ./data/test2016.csv..."
## [1] "dimensions of data in ./data/test2016.csv: 1,392 rows x 107 cols"
## USER_ID YOB Gender Income HouseholdStatus
## 1 2 1985 Female $25,001 - $50,000 Single (no kids)
## 2 3 1983 Male $50,000 - $74,999 Married (w/kids)
## 3 6 1995 Male $75,000 - $100,000 Single (no kids)
## 4 7 1980 Female $50,000 - $74,999 Single (no kids)
## 5 14 1980 Female Married (no kids)
## 6 28 1973 Male over $150,000 Married (no kids)
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1 Master's Degree Yes No Yes No No
## 2 Current Undergraduate No Yes Yes
## 3 Current K-12
## 4 Master's Degree Yes Yes No Yes Yes Yes
## 5 Current Undergraduate Yes No Yes No No
## 6 Master's Degree No Yes No Yes No No
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650 Q120472
## 1 Public No Yes Yes Yes No Yes Yes Science
## 2 Public No Yes No
## 3 No No No Yes No Yes Science
## 4 Public No Yes No Yes No Yes Yes Science
## 5 Public Yes Yes No Yes Yes No Yes Art
## 6 Public No Yes No Yes Yes Yes Yes Science
## Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892 Q118117
## 1 Study first Yes Yes Yes No Giving Yes No
## 2 Study first No Yes No
## 3 Try first No Yes No Yes Giving
## 4 Try first Yes No No Yes Giving Yes Yes
## 5 Try first Yes Yes Yes Yes Giving No No
## 6 Try first Yes Yes No No Giving No Yes
## Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1 Idealist No Yes Cool headed Odd hours Yes Happy
## 2
## 3
## 4 Idealist No No Cool headed Standard hours No Happy
## 5 Idealist No Yes Hot headed Standard hours Yes Happy
## 6 Pragmatist Yes No Hot headed Odd hours Yes Right
## Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610 Q115611
## 1 Yes Yes No Yes A.M. Yes End Yes No
## 2 Yes Yes P.M.
## 3 Yes
## 4 Yes No No Yes A.M. Yes Start Yes No
## 5 Yes Yes Yes No P.M. Yes End No No
## 6 Yes Yes Yes Yes P.M. End Yes Yes
## Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386 Q113992
## 1 Me No Yes No Yes Yes TMI
## 2 No Yes
## 3 Yes No Yes Yes No TMI No
## 4 Me Yes No Yes Yes Yes TMI No
## 5 Me No No No Yes No TMI No
## 6 Circumstances No Yes No Yes No TMI Yes
## Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270 Q111848
## 1 No Tunes People Yes Yes No Yes Yes
## 2 No No No Yes
## 3 No Tunes Technology Yes No Yes No
## 4 Yes Talk People No No Yes No Yes
## 5 Tunes Technology No Yes Yes Yes
## 6 No Talk Technology No Yes Yes No Yes
## Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855 Q108617
## 1 Supportive No Yes Cautious Yes Yes!
## 2 No Yes Cautious No Yes! No
## 3 No No No
## 4 Supportive No PC No Cautious Yes Yes! No
## 5 Supportive Yes Mac Yes Cautious No Yes! No
## 6 Demanding No PC Yes Cautious No Umm... No
## Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993 Q106997
## 1 Yes In-person Yes
## 2 Space No Yes Yes Yes Grrr people
## 3 Yes In-person No No Yes Yes Yay people!
## 4 Space No Online No No Yes Yes Yay people!
## 5 Space No In-person No No Yes No Grrr people
## 6 Space No In-person Yes Yes Yes Grrr people
## Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906
## 1
## 2 Yes No No Yes No Yes No No
## 3 Yes No Yes No No Yes Yes No No
## 4 No No No No No Yes Yes No No
## 5 No No No Yes Yes Yes Yes Yes No
## 6 Yes No Yes Yes No No No Yes Yes
## Q102674 Q102687 Q102289 Q102089 Q101162 Q101163 Q101596 Q100689
## 1 No
## 2 Rent Pessimist Dad
## 3 No No Yes Own Optimist Mom No No
## 4 No No No Own Optimist Dad No No
## 5 Yes No No Own Pessimist Mom No Yes
## 6 Yes Yes No Own Pessimist Mom No Yes
## Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 1 Yes Yes Yes
## 2 Yes Yes Yes
## 3 Yes Yes Nope No No No Yes Yes No Yes
## 4 Yes Yes Nope Yes No No No Yes No Yes
## 5 Yes Yes Nope Yes No No Yes No No Yes
## 6 Yes Yes Nope Yes No No Yes No No Yes
## Q98078 Q98197 Q96024
## 1
## 2 Yes No Yes
## 3 No Yes Yes
## 4 No No Yes
## 5 No No No
## 6 No No Yes
## USER_ID YOB Gender Income HouseholdStatus
## 503 2555 1956 Male over $150,000 Married (w/kids)
## 515 2616 1959 Male over $150,000 Married (w/kids)
## 857 4346 1990 Female $50,000 - $74,999
## 950 4814 1969 Male $75,000 - $100,000 Married (w/kids)
## 1207 6057 1937 Female $25,001 - $50,000 Married (no kids)
## 1255 6285 1976 Female $100,001 - $150,000 Married (no kids)
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 503 Bachelor's Degree No No No Yes No Yes
## 515 Bachelor's Degree
## 857 Bachelor's Degree
## 950 Bachelor's Degree Yes No Yes No No
## 1207 Bachelor's Degree No Yes
## 1255 Bachelor's Degree
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 503 Private No Yes No No Yes No Yes
## 515 No No
## 857 No Yes No No No No Yes
## 950 Public Yes Yes No Yes Yes No Yes
## 1207 Public No Yes No No No No
## 1255
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 503 Science Study first No Yes No Yes Giving Yes
## 515 Yes
## 857 Science Study first No No Yes No Receiving Yes
## 950 Science Study first No No No No Giving No
## 1207 Study first No No Yes Receiving Yes
## 1255
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797
## 503 No Pragmatist No No Cool headed Standard hours No
## 515 No Pragmatist No Yes Cool headed Standard hours No
## 857 Yes Pragmatist No No Cool headed Odd hours No
## 950 No Pragmatist No Yes Hot headed Odd hours Yes
## 1207 No Pragmatist No No Hot headed No
## 1255
## Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777
## 503 Happy Yes Yes No No A.M. Yes End
## 515 Right Yes Yes No Yes Yes
## 857 Right Yes Yes No No A.M. Yes Start
## 950 Happy Yes Yes Yes No P.M. Yes Start
## 1207 Happy Yes Yes No No A.M. Yes Start
## 1255 Yes No Yes A.M. Yes Start
## Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517
## 503 Yes Yes Me No No No Yes Yes
## 515 Yes No Me Yes No Yes Yes No
## 857 Yes No Me No No No Yes
## 950 Yes No Me Yes No Yes No No
## 1207 No No Circumstances Yes No Yes No Yes
## 1255 Yes No Circumstances No Yes No Yes Yes
## Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512
## 503 TMI Yes Yes Tunes People Yes No Yes
## 515 No Yes Talk Technology
## 857 Mysterious No No Tunes People No No No
## 950 Mysterious No No Tunes People Yes Yes Yes
## 1207 Yes No Talk Yes
## 1255 TMI Yes Yes Yes
## Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 503 No Yes Demanding No PC No Cautious
## 515 No Yes No Mac Yes
## 857 Yes Yes Supportive No Mac No Risk-friendly
## 950 No Yes Supportive Yes PC No Cautious
## 1207 Supportive No PC Cautious
## 1255 Yes Yes Demanding No Mac
## Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 503 No Umm... No Space No In-person No Yes
## 515
## 857 Yes Umm... No Space No In-person No Yes
## 950 No Yes! No Space No In-person No No
## 1207 Yes! No Space No In-person No Yes
## 1255
## Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 503 Yes Yes Yay people! Yes No No Yes No
## 515 No
## 857 No Yes Grrr people Yes No Yes No No
## 950 Yes No Grrr people Yes Yes No No No
## 1207 Yes Yes Yes
## 1255
## Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 503 No Yes No No No Yes No Own
## 515 Yes Yes
## 857 No Yes Yes No No Yes Yes Own
## 950 Yes Yes Yes No No Yes No Own
## 1207 Yes
## 1255
## Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010
## 503 Pessimist Mom Yes Yes No Yes Check! Yes
## 515 Check! Yes
## 857 Optimist Mom No Yes Yes No Nope Yes
## 950 Pessimist Mom Yes No No No Check! Yes
## 1207
## 1255
## Q99716 Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 503 No No Yes Yes No Yes Yes Yes Yes
## 515 No Yes Yes Yes No Yes Yes
## 857 No Yes Yes Yes No Yes No No No
## 950 No No Yes Yes No Yes No Yes Yes
## 1207
## 1255
## USER_ID YOB Gender Income HouseholdStatus
## 1387 6922 1988 Male $50,000 - $74,999 Single (no kids)
## 1388 6928 1977 Female $50,000 - $74,999 Domestic Partners (no kids)
## 1389 6930 1998 Female $100,001 - $150,000 Single (no kids)
## 1390 6941 1989 Male $25,001 - $50,000 Married (no kids)
## 1391 6946 1996 Male
## 1392 6947 NA Female
## EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1387 Master's Degree
## 1388 Master's Degree
## 1389 Current K-12 No No
## 1390 Bachelor's Degree
## 1391 Current K-12
## 1392 Yes Yes No No No No
## Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1387 Yes Yes Yes Yes Yes Yes
## 1388 Yes No Yes
## 1389 Public Yes Yes Yes Yes Yes Yes Yes
## 1390 Yes Yes No No No
## 1391 Yes No No Yes No Yes Yes
## 1392 Public Yes Yes No Yes Yes Yes Yes
## Q120472 Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1387 Science Try first No Yes Yes No Giving
## 1388 Art
## 1389 Art Study first Yes No Yes No Giving
## 1390
## 1391 Art Study first Yes Yes Yes No Giving
## 1392 Art No No No Yes Giving
## Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1387
## 1388
## 1389
## 1390
## 1391
## 1392
## 'data.frame': 1392 obs. of 20 variables:
## $ USER_ID : int 2 3 6 7 14 28 29 37 44 56 ...
## $ YOB : int 1985 1983 1995 1980 1980 1973 1968 1961 1989 1975 ...
## $ Gender : chr "Female" "Male" "Male" "Female" ...
## $ Income : chr "$25,001 - $50,000" "$50,000 - $74,999" "$75,000 - $100,000" "$50,000 - $74,999" ...
## $ HouseholdStatus: chr "Single (no kids)" "Married (w/kids)" "Single (no kids)" "Single (no kids)" ...
## $ EducationLevel : chr "Master's Degree" "Current Undergraduate" "Current K-12" "Master's Degree" ...
## $ Q124742 : chr "" "" "" "Yes" ...
## $ Q124122 : chr "Yes" "" "" "Yes" ...
## $ Q123464 : chr "No" "No" "" "No" ...
## $ Q123621 : chr "Yes" "" "" "Yes" ...
## $ Q122769 : chr "No" "Yes" "" "Yes" ...
## $ Q122770 : chr "No" "Yes" "" "Yes" ...
## $ Q122771 : chr "Public" "Public" "" "Public" ...
## $ Q122120 : chr "No" "No" "" "No" ...
## $ Q121699 : chr "Yes" "Yes" "No" "Yes" ...
## $ Q121700 : chr "Yes" "No" "No" "No" ...
## $ Q120978 : chr "Yes" "" "No" "Yes" ...
## $ Q121011 : chr "No" "" "Yes" "No" ...
## $ Q120379 : chr "Yes" "" "No" "Yes" ...
## $ Q120650 : chr "Yes" "" "Yes" "Yes" ...
## NULL
## 'data.frame': 1392 obs. of 20 variables:
## $ Q120012: chr "Yes" "No" "No" "Yes" ...
## $ Q120014: chr "Yes" "Yes" "Yes" "No" ...
## $ Q118117: chr "No" "" "" "Yes" ...
## $ Q118237: chr "Yes" "" "" "No" ...
## $ Q116953: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q116601: chr "Yes" "Yes" "" "No" ...
## $ Q116448: chr "Yes" "" "" "Yes" ...
## $ Q116197: chr "A.M." "P.M." "" "A.M." ...
## $ Q115899: chr "Me" "" "" "Me" ...
## $ Q114961: chr "Yes" "" "No" "No" ...
## $ Q113584: chr "People" "" "Technology" "People" ...
## $ Q113181: chr "Yes" "No" "Yes" "No" ...
## $ Q112512: chr "No" "" "Yes" "Yes" ...
## $ Q108950: chr "Cautious" "Cautious" "" "Cautious" ...
## $ Q108617: chr "" "No" "No" "No" ...
## $ Q108342: chr "In-person" "" "In-person" "Online" ...
## $ Q107491: chr "" "Yes" "Yes" "Yes" ...
## $ Q106272: chr "" "Yes" "Yes" "No" ...
## $ Q106389: chr "" "No" "Yes" "No" ...
## $ Q104996: chr "" "No" "Yes" "Yes" ...
## NULL
## 'data.frame': 1392 obs. of 21 variables:
## $ Q102674: chr "" "" "No" "No" ...
## $ Q102687: chr "" "" "No" "No" ...
## $ Q102289: chr "" "" "Yes" "No" ...
## $ Q102089: chr "" "Rent" "Own" "Own" ...
## $ Q101162: chr "" "Pessimist" "Optimist" "Optimist" ...
## $ Q101163: chr "" "Dad" "Mom" "Dad" ...
## $ Q101596: chr "" "" "No" "No" ...
## $ Q100689: chr "No" "" "No" "No" ...
## $ Q100680: chr "Yes" "" "Yes" "Yes" ...
## $ Q100562: chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q99982 : chr "" "" "Nope" "Nope" ...
## $ Q100010: chr "" "" "No" "Yes" ...
## $ Q99716 : chr "" "" "No" "No" ...
## $ Q99581 : chr "" "" "No" "No" ...
## $ Q99480 : chr "" "" "Yes" "No" ...
## $ Q98869 : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Q98578 : chr "" "" "No" "No" ...
## $ Q98059 : chr "" "Yes" "Yes" "Yes" ...
## $ Q98078 : chr "" "Yes" "No" "No" ...
## $ Q98197 : chr "" "No" "Yes" "No" ...
## $ Q96024 : chr "" "Yes" "Yes" "Yes" ...
## NULL## Warning in myprint_str_df(obsDf): [list output truncated]## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: YOB.Age.fctr..."
## [1] "Creating new feature: YOB.Age.dff..."
## [1] "Creating new feature: Gender.fctr..."
## [1] "Creating new feature: Income.fctr..."
## [1] "Creating new feature: Hhold.fctr..."
## [1] "Creating new feature: Edn.fctr..."
## [1] "Creating new feature: Q124742.fctr..."
## [1] "Creating new feature: Q124122.fctr..."
## [1] "Creating new feature: Q123621.fctr..."
## [1] "Creating new feature: Q123464.fctr..."
## [1] "Creating new feature: Q122771.fctr..."
## [1] "Creating new feature: Q122770.fctr..."
## [1] "Creating new feature: Q122769.fctr..."
## [1] "Creating new feature: Q122120.fctr..."
## [1] "Creating new feature: Q121700.fctr..."
## [1] "Creating new feature: Q121699.fctr..."
## [1] "Creating new feature: Q121011.fctr..."
## [1] "Creating new feature: Q120978.fctr..."
## [1] "Creating new feature: Q120650.fctr..."
## [1] "Creating new feature: Q120472.fctr..."
## [1] "Creating new feature: Q120379.fctr..."
## [1] "Creating new feature: Q120194.fctr..."
## [1] "Creating new feature: Q120014.fctr..."
## [1] "Creating new feature: Q120012.fctr..."
## [1] "Creating new feature: Q119851.fctr..."
## [1] "Creating new feature: Q119650.fctr..."
## [1] "Creating new feature: Q119334.fctr..."
## [1] "Creating new feature: Q118892.fctr..."
## [1] "Creating new feature: Q118237.fctr..."
## [1] "Creating new feature: Q118233.fctr..."
## [1] "Creating new feature: Q118232.fctr..."
## [1] "Creating new feature: Q118117.fctr..."
## [1] "Creating new feature: Q117193.fctr..."
## [1] "Creating new feature: Q117186.fctr..."
## [1] "Creating new feature: Q116797.fctr..."
## [1] "Creating new feature: Q116881.fctr..."
## [1] "Creating new feature: Q116953.fctr..."
## [1] "Creating new feature: Q116601.fctr..."
## [1] "Creating new feature: Q116441.fctr..."
## [1] "Creating new feature: Q116448.fctr..."
## [1] "Creating new feature: Q116197.fctr..."
## [1] "Creating new feature: Q115602.fctr..."
## [1] "Creating new feature: Q115777.fctr..."
## [1] "Creating new feature: Q115610.fctr..."
## [1] "Creating new feature: Q115611.fctr..."
## [1] "Creating new feature: Q115899.fctr..."
## [1] "Creating new feature: Q115390.fctr..."
## [1] "Creating new feature: Q115195.fctr..."
## [1] "Creating new feature: Q114961.fctr..."
## [1] "Creating new feature: Q114748.fctr..."
## [1] "Creating new feature: Q114517.fctr..."
## [1] "Creating new feature: Q114386.fctr..."
## [1] "Creating new feature: Q114152.fctr..."
## [1] "Creating new feature: Q113992.fctr..."
## [1] "Creating new feature: Q113583.fctr..."
## [1] "Creating new feature: Q113584.fctr..."
## [1] "Creating new feature: Q113181.fctr..."
## [1] "Creating new feature: Q112478.fctr..."
## [1] "Creating new feature: Q112512.fctr..."
## [1] "Creating new feature: Q112270.fctr..."
## [1] "Creating new feature: Q111848.fctr..."
## [1] "Creating new feature: Q111580.fctr..."
## [1] "Creating new feature: Q111220.fctr..."
## [1] "Creating new feature: Q110740.fctr..."
## [1] "Creating new feature: Q109367.fctr..."
## [1] "Creating new feature: Q109244.fctr..."
## [1] "Creating new feature: Q108950.fctr..."
## [1] "Creating new feature: Q108855.fctr..."
## [1] "Creating new feature: Q108617.fctr..."
## [1] "Creating new feature: Q108856.fctr..."
## [1] "Creating new feature: Q108754.fctr..."
## [1] "Creating new feature: Q108342.fctr..."
## [1] "Creating new feature: Q108343.fctr..."
## [1] "Creating new feature: Q107869.fctr..."
## [1] "Creating new feature: Q107491.fctr..."
## [1] "Creating new feature: Q106993.fctr..."
## [1] "Creating new feature: Q106997.fctr..."
## [1] "Creating new feature: Q106272.fctr..."
## [1] "Creating new feature: Q106388.fctr..."
## [1] "Creating new feature: Q106389.fctr..."
## [1] "Creating new feature: Q106042.fctr..."
## [1] "Creating new feature: Q105840.fctr..."
## [1] "Creating new feature: Q105655.fctr..."
## [1] "Creating new feature: Q104996.fctr..."
## [1] "Creating new feature: Q103293.fctr..."
## [1] "Creating new feature: Q102906.fctr..."
## [1] "Creating new feature: Q102674.fctr..."
## [1] "Creating new feature: Q102687.fctr..."
## [1] "Creating new feature: Q102289.fctr..."
## [1] "Creating new feature: Q102089.fctr..."
## [1] "Creating new feature: Q101162.fctr..."
## [1] "Creating new feature: Q101163.fctr..."
## [1] "Creating new feature: Q101596.fctr..."
## [1] "Creating new feature: Q100689.fctr..."
## [1] "Creating new feature: Q100680.fctr..."
## [1] "Creating new feature: Q100562.fctr..."
## [1] "Creating new feature: Q100010.fctr..."
## [1] "Creating new feature: Q99982.fctr..."
## [1] "Creating new feature: Q99716.fctr..."
## [1] "Creating new feature: Q99581.fctr..."
## [1] "Creating new feature: Q99480.fctr..."
## [1] "Creating new feature: Q98869.fctr..."
## [1] "Creating new feature: Q98578.fctr..."
## [1] "Creating new feature: Q98197.fctr..."
## [1] "Creating new feature: Q98059.fctr..."
## [1] "Creating new feature: Q98078.fctr..."
## [1] "Creating new feature: Q96024.fctr..."## [1] "Partition stats:"## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk## Party .src .n
## 1 Democrat Train 2951
## 2 Republican Train 2617
## 3 <NA> Test 1392
## Party .src .n
## 1 Democrat Train 2951
## 2 Republican Train 2617
## 3 <NA> Test 1392## Loading required package: RColorBrewer
## .src .n
## 1 Train 5568
## 2 Test 1392## [1] "Running glbObsDropCondition filter: (glbObsAll[, \"Q109244\"] != \"\")"
## [1] "Partition stats:"
## Party .src .n
## 1 Democrat Train 1171
## 2 Republican Train 1013
## 3 <NA> Test 547
## Party .src .n
## 1 Democrat Train 1171
## 2 Republican Train 1013
## 3 <NA> Test 547
## .src .n
## 1 Train 2184
## 2 Test 547## Loading required package: lazyeval## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## [1] "Found 0 duplicates by all features:"## NULL## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 5.750 13.273 7.524
## 2 inspect.data 2 0 0 13.274 NA NAStep 2.0: inspect data
## Warning: Removed 547 rows containing non-finite values (stat_count).## Loading required package: reshape2
## Party.Democrat Party.Republican Party.NA
## Test NA NA 547
## Train 1171 1013 NA
## Party.Democrat Party.Republican Party.NA
## Test NA NA 1
## Train 0.5361722 0.4638278 NA
## [1] "numeric data missing in : "
## YOB
## 239
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff
## 253
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Gender Income HouseholdStatus EducationLevel
## 88 665 316 521
## Party Q124742 Q124122 Q123464
## NA 2313 1954 1895
## Q123621 Q122769 Q122770 Q122771
## 1928 1855 1758 1745
## Q122120 Q121699 Q121700 Q120978
## 1719 1508 1543 1447
## Q121011 Q120379 Q120650 Q120472
## 1447 1485 1367 1504
## Q120194 Q120012 Q120014 Q119334
## 1657 1488 1641 1652
## Q119851 Q119650 Q118892 Q118117
## 1458 1522 1493 1635
## Q118232 Q118233 Q118237 Q117186
## 2006 1837 1789 1914
## Q117193 Q116797 Q116881 Q116953
## 1868 1939 1978 1953
## Q116601 Q116441 Q116448 Q116197
## 1847 1904 1927 1858
## Q115602 Q115777 Q115610 Q115611
## 1844 1943 1870 1754
## Q115899 Q115390 Q114961 Q114748
## 1956 1962 1905 1808
## Q115195 Q114517 Q114386 Q113992
## 1880 1870 1951 1849
## Q114152 Q113583 Q113584 Q113181
## 2031 1883 1901 1906
## Q112478 Q112512 Q112270 Q111848
## 2067 2004 2050 1872
## Q111580 Q111220 Q110740 Q109367
## 1993 1993 1949 2383
## Q108950 Q109244 Q108855 Q108617
## 2346 2731 2379 2265
## Q108856 Q108754 Q108342 Q108343
## 2388 2284 2262 2241
## Q107869 Q107491 Q106993 Q106997
## 2177 2125 2100 2113
## Q106272 Q106388 Q106389 Q106042
## 2084 2114 2140 2093
## Q105840 Q105655 Q104996 Q103293
## 2167 2042 2019 2042
## Q102906 Q102674 Q102687 Q102289
## 2116 2116 2038 2079
## Q102089 Q101162 Q101163 Q101596
## 2052 2094 2152 2104
## Q100689 Q100680 Q100562 Q99982
## 1969 2074 2080 2114
## Q100010 Q99716 Q99581 Q99480
## 2033 2071 2010 2016
## Q98869 Q98578 Q98059 Q98078
## 2088 2073 1984 2125
## Q98197 Q96024
## 2084 2065## Party Party.fctr .n
## 1 Democrat D 1171
## 2 Republican R 1013
## 3 <NA> <NA> 547## Warning: Removed 1 rows containing missing values (position_stack).
## Party.fctr.D Party.fctr.R Party.fctr.NA
## Test NA NA 547
## Train 1171 1013 NA
## Party.fctr.D Party.fctr.R Party.fctr.NA
## Test NA NA 1
## Train 0.5361722 0.4638278 NA
## [1] "elapsed Time (secs): 3.758000"## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "elapsed Time (secs): 131.599000"
## [1] "elapsed Time (secs): 131.599000"## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 13.274 150.664 137.391
## 3 scrub.data 2 1 1 150.665 NA NAStep 2.1: scrub data
```{r scrub.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## [1] "numeric data missing in : "
## YOB Party.fctr
## 239 547
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff
## 253
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Gender Income HouseholdStatus EducationLevel
## 88 665 316 521
## Party Q124742 Q124122 Q123464
## NA 2313 1954 1895
## Q123621 Q122769 Q122770 Q122771
## 1928 1855 1758 1745
## Q122120 Q121699 Q121700 Q120978
## 1719 1508 1543 1447
## Q121011 Q120379 Q120650 Q120472
## 1447 1485 1367 1504
## Q120194 Q120012 Q120014 Q119334
## 1657 1488 1641 1652
## Q119851 Q119650 Q118892 Q118117
## 1458 1522 1493 1635
## Q118232 Q118233 Q118237 Q117186
## 2006 1837 1789 1914
## Q117193 Q116797 Q116881 Q116953
## 1868 1939 1978 1953
## Q116601 Q116441 Q116448 Q116197
## 1847 1904 1927 1858
## Q115602 Q115777 Q115610 Q115611
## 1844 1943 1870 1754
## Q115899 Q115390 Q114961 Q114748
## 1956 1962 1905 1808
## Q115195 Q114517 Q114386 Q113992
## 1880 1870 1951 1849
## Q114152 Q113583 Q113584 Q113181
## 2031 1883 1901 1906
## Q112478 Q112512 Q112270 Q111848
## 2067 2004 2050 1872
## Q111580 Q111220 Q110740 Q109367
## 1993 1993 1949 2383
## Q108950 Q109244 Q108855 Q108617
## 2346 2731 2379 2265
## Q108856 Q108754 Q108342 Q108343
## 2388 2284 2262 2241
## Q107869 Q107491 Q106993 Q106997
## 2177 2125 2100 2113
## Q106272 Q106388 Q106389 Q106042
## 2084 2114 2140 2093
## Q105840 Q105655 Q104996 Q103293
## 2167 2042 2019 2042
## Q102906 Q102674 Q102687 Q102289
## 2116 2116 2038 2079
## Q102089 Q101162 Q101163 Q101596
## 2052 2094 2152 2104
## Q100689 Q100680 Q100562 Q99982
## 1969 2074 2080 2114
## Q100010 Q99716 Q99581 Q99480
## 2033 2071 2010 2016
## Q98869 Q98578 Q98059 Q98078
## 2088 2073 1984 2125
## Q98197 Q96024
## 2084 2065## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 150.665 185.534 34.87
## 4 transform.data 2 2 2 185.535 NA NAStep 2.2: transform data
## label step_major step_minor label_minor bgn end
## 4 transform.data 2 2 2 185.535 185.577
## 5 extract.features 3 0 0 185.577 NA
## elapsed
## 4 0.042
## 5 NAStep 3.0: extract features
## label step_major step_minor label_minor bgn
## 5 extract.features 3 0 0 185.577
## 6 extract.features.datetime 3 1 1 185.597
## end elapsed
## 5 185.596 0.019
## 6 NA NAStep 3.1: extract features datetime
## label step_major step_minor label_minor bgn
## 1 extract.features.datetime.bgn 1 0 0 185.624
## end elapsed
## 1 NA NA## label step_major step_minor label_minor bgn
## 6 extract.features.datetime 3 1 1 185.597
## 7 extract.features.image 3 2 2 185.637
## end elapsed
## 6 185.636 0.039
## 7 NA NAStep 3.2: extract features image
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end
## 1 extract.features.image.bgn 1 0 0 185.668 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 185.668
## 2 extract.features.image.end 2 0 0 185.679
## end elapsed
## 1 185.678 0.01
## 2 NA NA## label step_major step_minor label_minor bgn
## 1 extract.features.image.bgn 1 0 0 185.668
## 2 extract.features.image.end 2 0 0 185.679
## end elapsed
## 1 185.678 0.01
## 2 NA NA## label step_major step_minor label_minor bgn end
## 7 extract.features.image 3 2 2 185.637 185.689
## 8 extract.features.price 3 3 3 185.689 NA
## elapsed
## 7 0.052
## 8 NAStep 3.3: extract features price
## label step_major step_minor label_minor bgn end
## 1 extract.features.price.bgn 1 0 0 185.714 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 8 extract.features.price 3 3 3 185.689 185.723
## 9 extract.features.text 3 4 4 185.724 NA
## elapsed
## 8 0.035
## 9 NAStep 3.4: extract features text
## label step_major step_minor label_minor bgn end
## 1 extract.features.text.bgn 1 0 0 185.779 NA
## elapsed
## 1 NA## Warning in rm(tmp_allobs_df): object 'tmp_allobs_df' not found## Warning in rm(tmp_trnobs_df): object 'tmp_trnobs_df' not found## label step_major step_minor label_minor bgn
## 9 extract.features.text 3 4 4 185.724
## 10 extract.features.string 3 5 5 185.795
## end elapsed
## 9 185.794 0.07
## 10 NA NAStep 3.5: extract features string
## label step_major step_minor label_minor bgn
## 1 extract.features.string.bgn 1 0 0 185.831
## end elapsed
## 1 NA NA## label step_major step_minor
## 1 extract.features.string.bgn 1 0
## 2 extract.features.stringfactorize.str.vars 2 0
## label_minor bgn end elapsed
## 1 0 185.831 185.842 0.011
## 2 0 185.842 NA NA## Gender Income HouseholdStatus EducationLevel
## "Gender" "Income" "HouseholdStatus" "EducationLevel"
## Party Q124742 Q124122 Q123464
## "Party" "Q124742" "Q124122" "Q123464"
## Q123621 Q122769 Q122770 Q122771
## "Q123621" "Q122769" "Q122770" "Q122771"
## Q122120 Q121699 Q121700 Q120978
## "Q122120" "Q121699" "Q121700" "Q120978"
## Q121011 Q120379 Q120650 Q120472
## "Q121011" "Q120379" "Q120650" "Q120472"
## Q120194 Q120012 Q120014 Q119334
## "Q120194" "Q120012" "Q120014" "Q119334"
## Q119851 Q119650 Q118892 Q118117
## "Q119851" "Q119650" "Q118892" "Q118117"
## Q118232 Q118233 Q118237 Q117186
## "Q118232" "Q118233" "Q118237" "Q117186"
## Q117193 Q116797 Q116881 Q116953
## "Q117193" "Q116797" "Q116881" "Q116953"
## Q116601 Q116441 Q116448 Q116197
## "Q116601" "Q116441" "Q116448" "Q116197"
## Q115602 Q115777 Q115610 Q115611
## "Q115602" "Q115777" "Q115610" "Q115611"
## Q115899 Q115390 Q114961 Q114748
## "Q115899" "Q115390" "Q114961" "Q114748"
## Q115195 Q114517 Q114386 Q113992
## "Q115195" "Q114517" "Q114386" "Q113992"
## Q114152 Q113583 Q113584 Q113181
## "Q114152" "Q113583" "Q113584" "Q113181"
## Q112478 Q112512 Q112270 Q111848
## "Q112478" "Q112512" "Q112270" "Q111848"
## Q111580 Q111220 Q110740 Q109367
## "Q111580" "Q111220" "Q110740" "Q109367"
## Q108950 Q109244 Q108855 Q108617
## "Q108950" "Q109244" "Q108855" "Q108617"
## Q108856 Q108754 Q108342 Q108343
## "Q108856" "Q108754" "Q108342" "Q108343"
## Q107869 Q107491 Q106993 Q106997
## "Q107869" "Q107491" "Q106993" "Q106997"
## Q106272 Q106388 Q106389 Q106042
## "Q106272" "Q106388" "Q106389" "Q106042"
## Q105840 Q105655 Q104996 Q103293
## "Q105840" "Q105655" "Q104996" "Q103293"
## Q102906 Q102674 Q102687 Q102289
## "Q102906" "Q102674" "Q102687" "Q102289"
## Q102089 Q101162 Q101163 Q101596
## "Q102089" "Q101162" "Q101163" "Q101596"
## Q100689 Q100680 Q100562 Q99982
## "Q100689" "Q100680" "Q100562" "Q99982"
## Q100010 Q99716 Q99581 Q99480
## "Q100010" "Q99716" "Q99581" "Q99480"
## Q98869 Q98578 Q98059 Q98078
## "Q98869" "Q98578" "Q98059" "Q98078"
## Q98197 Q96024 .src
## "Q98197" "Q96024" ".src"## label step_major step_minor label_minor bgn
## 10 extract.features.string 3 5 5 185.795
## 11 extract.features.end 3 6 6 185.865
## end elapsed
## 10 185.864 0.07
## 11 NA NAStep 3.6: extract features end
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## label step_major step_minor label_minor bgn end
## 11 extract.features.end 3 6 6 185.865 186.754
## 12 manage.missing.data 4 0 0 186.754 NA
## elapsed
## 11 0.889
## 12 NAStep 4.0: manage missing data
## [1] "numeric data missing in : "
## YOB Party.fctr
## 239 547
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff
## 253
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Gender Income HouseholdStatus EducationLevel
## 88 665 316 521
## Party Q124742 Q124122 Q123464
## NA 2313 1954 1895
## Q123621 Q122769 Q122770 Q122771
## 1928 1855 1758 1745
## Q122120 Q121699 Q121700 Q120978
## 1719 1508 1543 1447
## Q121011 Q120379 Q120650 Q120472
## 1447 1485 1367 1504
## Q120194 Q120012 Q120014 Q119334
## 1657 1488 1641 1652
## Q119851 Q119650 Q118892 Q118117
## 1458 1522 1493 1635
## Q118232 Q118233 Q118237 Q117186
## 2006 1837 1789 1914
## Q117193 Q116797 Q116881 Q116953
## 1868 1939 1978 1953
## Q116601 Q116441 Q116448 Q116197
## 1847 1904 1927 1858
## Q115602 Q115777 Q115610 Q115611
## 1844 1943 1870 1754
## Q115899 Q115390 Q114961 Q114748
## 1956 1962 1905 1808
## Q115195 Q114517 Q114386 Q113992
## 1880 1870 1951 1849
## Q114152 Q113583 Q113584 Q113181
## 2031 1883 1901 1906
## Q112478 Q112512 Q112270 Q111848
## 2067 2004 2050 1872
## Q111580 Q111220 Q110740 Q109367
## 1993 1993 1949 2383
## Q108950 Q109244 Q108855 Q108617
## 2346 2731 2379 2265
## Q108856 Q108754 Q108342 Q108343
## 2388 2284 2262 2241
## Q107869 Q107491 Q106993 Q106997
## 2177 2125 2100 2113
## Q106272 Q106388 Q106389 Q106042
## 2084 2114 2140 2093
## Q105840 Q105655 Q104996 Q103293
## 2167 2042 2019 2042
## Q102906 Q102674 Q102687 Q102289
## 2116 2116 2038 2079
## Q102089 Q101162 Q101163 Q101596
## 2052 2094 2152 2104
## Q100689 Q100680 Q100562 Q99982
## 1969 2074 2080 2114
## Q100010 Q99716 Q99581 Q99480
## 2033 2071 2010 2016
## Q98869 Q98578 Q98059 Q98078
## 2088 2073 1984 2125
## Q98197 Q96024
## 2084 2065## [1] "numeric data missing in : "
## YOB Party.fctr
## 239 547
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff
## 253
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Gender Income HouseholdStatus EducationLevel
## 88 665 316 521
## Party Q124742 Q124122 Q123464
## NA 2313 1954 1895
## Q123621 Q122769 Q122770 Q122771
## 1928 1855 1758 1745
## Q122120 Q121699 Q121700 Q120978
## 1719 1508 1543 1447
## Q121011 Q120379 Q120650 Q120472
## 1447 1485 1367 1504
## Q120194 Q120012 Q120014 Q119334
## 1657 1488 1641 1652
## Q119851 Q119650 Q118892 Q118117
## 1458 1522 1493 1635
## Q118232 Q118233 Q118237 Q117186
## 2006 1837 1789 1914
## Q117193 Q116797 Q116881 Q116953
## 1868 1939 1978 1953
## Q116601 Q116441 Q116448 Q116197
## 1847 1904 1927 1858
## Q115602 Q115777 Q115610 Q115611
## 1844 1943 1870 1754
## Q115899 Q115390 Q114961 Q114748
## 1956 1962 1905 1808
## Q115195 Q114517 Q114386 Q113992
## 1880 1870 1951 1849
## Q114152 Q113583 Q113584 Q113181
## 2031 1883 1901 1906
## Q112478 Q112512 Q112270 Q111848
## 2067 2004 2050 1872
## Q111580 Q111220 Q110740 Q109367
## 1993 1993 1949 2383
## Q108950 Q109244 Q108855 Q108617
## 2346 2731 2379 2265
## Q108856 Q108754 Q108342 Q108343
## 2388 2284 2262 2241
## Q107869 Q107491 Q106993 Q106997
## 2177 2125 2100 2113
## Q106272 Q106388 Q106389 Q106042
## 2084 2114 2140 2093
## Q105840 Q105655 Q104996 Q103293
## 2167 2042 2019 2042
## Q102906 Q102674 Q102687 Q102289
## 2116 2116 2038 2079
## Q102089 Q101162 Q101163 Q101596
## 2052 2094 2152 2104
## Q100689 Q100680 Q100562 Q99982
## 1969 2074 2080 2114
## Q100010 Q99716 Q99581 Q99480
## 2033 2071 2010 2016
## Q98869 Q98578 Q98059 Q98078
## 2088 2073 1984 2125
## Q98197 Q96024
## 2084 2065## label step_major step_minor label_minor bgn end
## 12 manage.missing.data 4 0 0 186.754 187.379
## 13 cluster.data 5 0 0 187.380 NA
## elapsed
## 12 0.626
## 13 NAStep 5.0: cluster data
```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## Loading required package: proxy##
## Attaching package: 'proxy'## The following objects are masked from 'package:stats':
##
## as.dist, dist## The following object is masked from 'package:base':
##
## as.matrix## Loading required package: dynamicTreeCut## Loading required package: entropy## Loading required package: tidyr## Loading required package: ggdendro## [1] "Clustering features: "## Warning in cor(data.matrix(glbObsAll[glbObsAll$.src == "Train",
## glbFeatsCluster]), : the standard deviation is zero## abs.cor.y
## Q113181.fctr 0.04357559
## Q102089.fctr 0.04804567
## Q100689.fctr 0.05185690
## Q113583.fctr 0.05306280
## Q101163.fctr 0.07163663
## [1] " .rnorm cor: 0.0268"
## [1] " Clustering entropy measure: Party.fctr"
## [1] "glbObsAll Entropy: 0.6905"
## Q109244.fctr .clusterid Q109244.fctr.clusterid D R .entropy .knt
## 1 NA 1 NA_1 1171 1013 0.690528 2184
## [1] "glbObsAll$Q109244.fctr Entropy: 0.6905 (100.0000 pct)"## [1] "Category: NA"
## [1] "max distance(0.9810) pair:"
## USER_ID Party.fctr Q109244.fctr Q124742.fctr Q124122.fctr
## 5090 6355 R NA Yes Yes
## 6014 2335 <NA> NA NA NA
## Q123621.fctr Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr
## 5090 No No Pc Yes Yes
## 6014 NA NA NA NA NA
## Q122120.fctr Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr
## 5090 No Yes Yes Yes No
## 6014 NA NA NA NA NA
## Q120650.fctr Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr
## 5090 Yes Science No Study first No
## 6014 NA NA NA NA NA
## Q120012.fctr Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr
## 5090 No Yes Giving Yes Yes
## 6014 NA NA NA NA NA
## Q118237.fctr Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr
## 5090 Yes Yes Id Yes Standard hours
## 6014 NA NA NA NA NA
## Q117186.fctr Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr
## 5090 Cool headed Yes Happy Yes Yes
## 6014 NA NA NA NA NA
## Q116441.fctr Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr
## 5090 No Yes P.M. Yes End
## 6014 NA NA P.M. NA NA
## Q115610.fctr Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr
## 5090 Yes No Me No Yes
## 6014 NA NA NA NA NA
## Q114961.fctr Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr
## 5090 Yes Yes No Mysterious NA
## 6014 NA NA NA NA NA
## Q113992.fctr Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr
## 5090 NA NA NA NA NA
## 6014 NA NA NA NA Yes
## Q112512.fctr Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr
## 5090 NA NA NA NA NA
## 6014 Yes No Yes Supportive No
## Q110740.fctr Q109367.fctr Q109244.fctr.1 Q108950.fctr Q108855.fctr
## 5090 NA NA NA NA NA
## 6014 PC NA NA Cautious Umm...
## Q108617.fctr Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr
## 5090 NA NA NA NA NA
## 6014 No Space No Online Yes
## Q107869.fctr Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr
## 5090 NA NA NA NA NA
## 6014 No Yes Yes Yy Yes
## Q106388.fctr Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr
## 5090 NA NA NA NA NA
## 6014 No No Yes No Yes
## Q104996.fctr Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr
## 5090 NA NA NA NA NA
## 6014 No Yes No No Yes
## Q102289.fctr Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr
## 5090 NA NA NA NA NA
## 6014 No Own Pessimist Dad Yes
## Q100689.fctr Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr
## 5090 NA NA NA NA NA
## 6014 Yes Yes No Yes Nope
## Q99716.fctr Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr
## 5090 NA NA NA NA NA
## 6014 No No Yes NA NA
## Q98197.fctr Q98059.fctr Q98078.fctr Q96024.fctr
## 5090 NA NA NA NA
## 6014 NA NA NA Yes
## [1] "min distance(0.9648) pair:"
## USER_ID Party.fctr Q109244.fctr Q124742.fctr Q124122.fctr
## 5901 1696 <NA> NA NA NA
## 6327 3843 <NA> NA NA NA
## Q123621.fctr Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q122120.fctr Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q120650.fctr Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q120012.fctr Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q118237.fctr Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q117186.fctr Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q116441.fctr Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q115610.fctr Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q114961.fctr Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q113992.fctr Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q112512.fctr Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr
## 5901 NA NA NA Demanding NA
## 6327 NA NA NA NA NA
## Q110740.fctr Q109367.fctr Q109244.fctr.1 Q108950.fctr Q108855.fctr
## 5901 NA NA NA NA NA
## 6327 Mac NA NA NA NA
## Q108617.fctr Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr
## 5901 No Socialize NA NA NA
## 6327 NA NA NA NA NA
## Q107869.fctr Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr
## 5901 Yes NA NA NA NA
## 6327 No No NA Gr NA
## Q106388.fctr Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q104996.fctr Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr
## 5901 NA NA NA NA NA
## 6327 Yes NA No No No
## Q102289.fctr Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr
## 5901 NA NA NA Mom NA
## 6327 No NA Pessimist Mom NA
## Q100689.fctr Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q99716.fctr Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr
## 5901 NA NA NA NA NA
## 6327 NA NA NA NA NA
## Q98197.fctr Q98059.fctr Q98078.fctr Q96024.fctr
## 5901 NA Only-child NA NA
## 6327 NA NA NA NA
## Q109244.fctr .clusterid Q109244.fctr.clusterid D R .entropy .knt
## 1 NA 1 NA_1 381 338 0.6913578 719
## 2 NA 2 NA_2 303 277 0.6921421 580
## 3 NA 3 NA_3 282 212 0.6830738 494
## 4 NA 4 NA_4 205 186 0.6919661 391
## [1] "glbObsAll$Q109244.fctr$.clusterid Entropy: 0.6898 (99.8947 pct)"## label step_major step_minor label_minor bgn
## 13 cluster.data 5 0 0 187.380
## 14 partition.data.training 6 0 0 245.044
## end elapsed
## 13 245.043 57.663
## 14 NA NAStep 6.0: partition data training
## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 0.09 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 0.09 secs"
## [1] "lclgetMatrixSimilarity: duration: 14.373000 secs"## Loading required package: sampling##
## Attaching package: 'sampling'## The following object is masked from 'package:caret':
##
## cluster## Stratum 1
##
## Population total and number of selected units: 1171 235
## Stratum 2
##
## Population total and number of selected units: 1013 203
## Number of strata 2
## Total number of selected units 438
## [1] "lclgetMatrixSimilarity: duration: 10.545000 secs"
## [1] "lclgetMatrixSimilarity: duration: 4.019000 secs"
## [1] "lclgetMatrixSimilarity: duration: 3.618000 secs"
## [1] "lclgetMatrixSimilarity: duration: 8.807000 secs"## Warning: Removed 6 rows containing missing values (geom_path).## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?## Warning: Removed 12 rows containing missing values (geom_point).
## [1] "Similarity of partitions:"
## cor cosineSmy obs.x obs.y
## 1 0.9999863 0.8824816 OOB Fit
## 2 0.9999864 0.9177603 OOB New
## 3 0.9999864 0.8303240 Fit New## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 42.68 secs"## Party.Democrat Party.Republican Party.NA
## NA NA 547
## Fit 936 810 NA
## OOB 235 203 NA
## Party.Democrat Party.Republican Party.NA
## NA NA 1
## Fit 0.5360825 0.4639175 NA
## OOB 0.5365297 0.4634703 NA
## Q109244.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 1 NA 1746 438 547 1 1
## .freqRatio.Tst
## 1 1## [1] "glbObsAll: "## [1] 2731 222## [1] "glbObsTrn: "## [1] 2184 222## [1] "glbObsFit: "## [1] 1746 221## [1] "glbObsOOB: "## [1] 438 221## [1] "glbObsNew: "## [1] 547 221## [1] "partition.data.training chunk: teardown: elapsed: 43.27 secs"## label step_major step_minor label_minor bgn
## 14 partition.data.training 6 0 0 245.044
## 15 select.features 7 0 0 288.391
## end elapsed
## 14 288.391 43.347
## 15 NA NAStep 7.0: select features
```{r select.features, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## [1] "cor(Q98059.fctr, Q98078.fctr)=0.7770"
## [1] "cor(Party.fctr, Q98059.fctr)=-0.0411"
## [1] "cor(Party.fctr, Q98078.fctr)=-0.0435"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98059.fctr as highly correlated with Q98078.fctr## [1] "cor(Q100562.fctr, Q100680.fctr)=0.7680"
## [1] "cor(Party.fctr, Q100562.fctr)=-0.0339"
## [1] "cor(Party.fctr, Q100680.fctr)=-0.0273"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100680.fctr as highly correlated with Q100562.fctr## [1] "cor(Q113583.fctr, Q113584.fctr)=0.7653"
## [1] "cor(Party.fctr, Q113583.fctr)=-0.0531"
## [1] "cor(Party.fctr, Q113584.fctr)=-0.0342"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q113584.fctr as highly correlated with Q113583.fctr## [1] "cor(Q102674.fctr, Q102687.fctr)=0.7442"
## [1] "cor(Party.fctr, Q102674.fctr)=-0.0418"
## [1] "cor(Party.fctr, Q102687.fctr)=-0.0306"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q102687.fctr as highly correlated with Q102674.fctr## [1] "cor(Q100562.fctr, Q100689.fctr)=0.7006"
## [1] "cor(Party.fctr, Q100562.fctr)=-0.0339"
## [1] "cor(Party.fctr, Q100689.fctr)=-0.0519"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100562.fctr as highly correlated with Q100689.fctr## cor.y exclude.as.feat cor.y.abs cor.high.X
## Gender.fctr 0.0909665772 0 0.0909665772 <NA>
## Q113181.fctr 0.0435755897 0 0.0435755897 <NA>
## .pos 0.0413144073 1 0.0413144073 <NA>
## USER_ID 0.0412669796 1 0.0412669796 <NA>
## Q120472.fctr 0.0373511418 0 0.0373511418 <NA>
## Q115611.fctr 0.0373054801 0 0.0373054801 <NA>
## Q120650.fctr 0.0358710311 0 0.0358710311 <NA>
## Q118237.fctr 0.0271389945 0 0.0271389945 <NA>
## .rnorm 0.0268489499 0 0.0268489499 <NA>
## Q122120.fctr 0.0234623979 0 0.0234623979 <NA>
## Q110740.fctr 0.0213122928 0 0.0213122928 <NA>
## Q122770.fctr 0.0203817957 0 0.0203817957 <NA>
## Q118117.fctr 0.0200889054 0 0.0200889054 <NA>
## Income.fctr 0.0178852251 0 0.0178852251 <NA>
## Q116441.fctr 0.0177162919 0 0.0177162919 <NA>
## Q118233.fctr 0.0176482250 0 0.0176482250 <NA>
## Q106272.fctr 0.0166660459 0 0.0166660459 <NA>
## Q119650.fctr 0.0154160890 0 0.0154160890 <NA>
## Q124742.fctr 0.0148384193 0 0.0148384193 <NA>
## Q122771.fctr 0.0146492330 0 0.0146492330 <NA>
## Q99480.fctr 0.0141594473 0 0.0141594473 <NA>
## Q116197.fctr 0.0130225570 0 0.0130225570 <NA>
## Q116881.fctr 0.0127923944 0 0.0127923944 <NA>
## Q101596.fctr 0.0122700322 0 0.0122700322 <NA>
## Q122769.fctr 0.0120730754 0 0.0120730754 <NA>
## Q108855.fctr 0.0116199609 0 0.0116199609 <NA>
## Q120014.fctr 0.0100200811 0 0.0100200811 <NA>
## Q119334.fctr 0.0097611771 0 0.0097611771 <NA>
## Q106993.fctr 0.0088906471 0 0.0088906471 <NA>
## Q107869.fctr 0.0084600631 0 0.0084600631 <NA>
## YOB 0.0065731919 1 0.0065731919 <NA>
## Q121011.fctr 0.0064795771 0 0.0064795771 <NA>
## Q117186.fctr 0.0061297032 0 0.0061297032 <NA>
## Q106997.fctr 0.0047472923 0 0.0047472923 <NA>
## YOB.Age.dff 0.0039888175 0 0.0039888175 <NA>
## Q108617.fctr 0.0034142713 0 0.0034142713 <NA>
## Q98197.fctr 0.0033385631 0 0.0033385631 <NA>
## Q106042.fctr 0.0028257871 0 0.0028257871 <NA>
## Q115777.fctr 0.0021874038 0 0.0021874038 <NA>
## Q123621.fctr 0.0020333068 0 0.0020333068 <NA>
## Q106388.fctr 0.0019532137 0 0.0019532137 <NA>
## Q114152.fctr -0.0002141693 0 0.0002141693 <NA>
## Q124122.fctr -0.0005523953 0 0.0005523953 <NA>
## Q120194.fctr -0.0008725662 0 0.0008725662 <NA>
## Q116797.fctr -0.0009782776 0 0.0009782776 <NA>
## Q105655.fctr -0.0019537389 0 0.0019537389 <NA>
## Q115899.fctr -0.0040294642 0 0.0040294642 <NA>
## Q116448.fctr -0.0042193065 0 0.0042193065 <NA>
## Q117193.fctr -0.0045436986 0 0.0045436986 <NA>
## Q108754.fctr -0.0052510790 0 0.0052510790 <NA>
## Q108856.fctr -0.0057486122 0 0.0057486122 <NA>
## YOB.Age.fctr -0.0071871098 0 0.0071871098 <NA>
## Q123464.fctr -0.0073497112 0 0.0073497112 <NA>
## Q99581.fctr -0.0075725773 0 0.0075725773 <NA>
## Q114961.fctr -0.0078051581 0 0.0078051581 <NA>
## Q104996.fctr -0.0087935260 0 0.0087935260 <NA>
## Q108343.fctr -0.0093294049 0 0.0093294049 <NA>
## Q120012.fctr -0.0094832005 0 0.0094832005 <NA>
## Q120978.fctr -0.0095190624 0 0.0095190624 <NA>
## .clusterid -0.0103251988 1 0.0103251988 <NA>
## .clusterid.fctr -0.0103251988 0 0.0103251988 <NA>
## Q98578.fctr -0.0127194176 0 0.0127194176 <NA>
## Q103293.fctr -0.0127467568 0 0.0127467568 <NA>
## Q106389.fctr -0.0127995068 0 0.0127995068 <NA>
## Q98869.fctr -0.0141131536 0 0.0141131536 <NA>
## Q112512.fctr -0.0148254430 0 0.0148254430 <NA>
## Q116953.fctr -0.0150205968 0 0.0150205968 <NA>
## Q100010.fctr -0.0157954167 0 0.0157954167 <NA>
## Q111220.fctr -0.0161563341 0 0.0161563341 <NA>
## Q102906.fctr -0.0162667502 0 0.0162667502 <NA>
## Q121700.fctr -0.0162998394 0 0.0162998394 <NA>
## Q112478.fctr -0.0164349791 0 0.0164349791 <NA>
## Q115610.fctr -0.0179375585 0 0.0179375585 <NA>
## Q119851.fctr -0.0188165770 0 0.0188165770 <NA>
## Q114517.fctr -0.0194814883 0 0.0194814883 <NA>
## Q118892.fctr -0.0197340603 0 0.0197340603 <NA>
## Q115602.fctr -0.0202866077 0 0.0202866077 <NA>
## Q120379.fctr -0.0203016988 0 0.0203016988 <NA>
## Q107491.fctr -0.0205240116 0 0.0205240116 <NA>
## Q114748.fctr -0.0209202111 0 0.0209202111 <NA>
## Q99982.fctr -0.0215133899 0 0.0215133899 <NA>
## Q113992.fctr -0.0222394292 0 0.0222394292 <NA>
## Q115390.fctr -0.0224688906 0 0.0224688906 <NA>
## Q118232.fctr -0.0257663213 0 0.0257663213 <NA>
## Q96024.fctr -0.0265018957 0 0.0265018957 <NA>
## Q115195.fctr -0.0271738479 0 0.0271738479 <NA>
## Q121699.fctr -0.0273324911 0 0.0273324911 <NA>
## Q100680.fctr -0.0273415528 0 0.0273415528 Q100562.fctr
## Q111580.fctr -0.0274150724 0 0.0274150724 <NA>
## Q102289.fctr -0.0285292574 0 0.0285292574 <NA>
## Q102687.fctr -0.0306196219 0 0.0306196219 Q102674.fctr
## Q105840.fctr -0.0307993280 0 0.0307993280 <NA>
## Q101162.fctr -0.0310084074 0 0.0310084074 <NA>
## Q108950.fctr -0.0317261524 0 0.0317261524 <NA>
## Q116601.fctr -0.0325709549 0 0.0325709549 <NA>
## Q108342.fctr -0.0332508344 0 0.0332508344 <NA>
## Q100562.fctr -0.0338636276 0 0.0338636276 Q100689.fctr
## Q113584.fctr -0.0341810079 0 0.0341810079 Q113583.fctr
## Q109367.fctr -0.0343630284 0 0.0343630284 <NA>
## Q99716.fctr -0.0374467543 0 0.0374467543 <NA>
## Hhold.fctr -0.0382423557 0 0.0382423557 <NA>
## Q112270.fctr -0.0396676511 0 0.0396676511 <NA>
## Q98059.fctr -0.0411225217 0 0.0411225217 Q98078.fctr
## Q111848.fctr -0.0412349958 0 0.0412349958 <NA>
## Q102674.fctr -0.0417938234 0 0.0417938234 <NA>
## Q114386.fctr -0.0423163811 0 0.0423163811 <NA>
## Q98078.fctr -0.0434942851 0 0.0434942851 <NA>
## Q102089.fctr -0.0480456671 0 0.0480456671 <NA>
## Edn.fctr -0.0493632201 0 0.0493632201 <NA>
## Q100689.fctr -0.0518568959 0 0.0518568959 <NA>
## Q113583.fctr -0.0530628021 0 0.0530628021 <NA>
## Q101163.fctr -0.0716366284 0 0.0716366284 <NA>
## Q109244.fctr NA 0 NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## Gender.fctr 1.450753 0.13736264 FALSE FALSE FALSE
## Q113181.fctr 3.968421 0.13736264 FALSE FALSE FALSE
## .pos 1.000000 100.00000000 FALSE FALSE FALSE
## USER_ID 1.000000 100.00000000 FALSE FALSE FALSE
## Q120472.fctr 1.896389 0.13736264 FALSE FALSE FALSE
## Q115611.fctr 2.705996 0.13736264 FALSE FALSE FALSE
## Q120650.fctr 1.112016 0.13736264 FALSE FALSE FALSE
## Q118237.fctr 3.284065 0.13736264 FALSE FALSE FALSE
## .rnorm 1.000000 100.00000000 FALSE FALSE FALSE
## Q122120.fctr 2.320883 0.13736264 FALSE FALSE TRUE
## Q110740.fctr 4.361345 0.13736264 FALSE FALSE TRUE
## Q122770.fctr 3.025974 0.13736264 FALSE FALSE TRUE
## Q118117.fctr 2.587649 0.13736264 FALSE FALSE TRUE
## Income.fctr 1.544160 0.32051282 FALSE FALSE TRUE
## Q116441.fctr 3.578824 0.13736264 FALSE FALSE TRUE
## Q118233.fctr 2.757576 0.13736264 FALSE FALSE TRUE
## Q106272.fctr 4.321149 0.13736264 FALSE FALSE TRUE
## Q119650.fctr 1.731915 0.13736264 FALSE FALSE TRUE
## Q124742.fctr 8.808612 0.13736264 FALSE FALSE TRUE
## Q122771.fctr 2.180534 0.13736264 FALSE FALSE TRUE
## Q99480.fctr 3.603139 0.13736264 FALSE FALSE TRUE
## Q116197.fctr 3.091858 0.13736264 FALSE FALSE TRUE
## Q116881.fctr 3.634259 0.13736264 FALSE FALSE TRUE
## Q101596.fctr 5.100304 0.13736264 FALSE FALSE TRUE
## Q122769.fctr 3.500000 0.13736264 FALSE FALSE TRUE
## Q108855.fctr 11.674847 0.13736264 FALSE FALSE TRUE
## Q120014.fctr 2.410681 0.13736264 FALSE FALSE TRUE
## Q119334.fctr 2.779193 0.13736264 FALSE FALSE TRUE
## Q106993.fctr 3.783599 0.13736264 FALSE FALSE TRUE
## Q107869.fctr 7.161826 0.13736264 FALSE FALSE TRUE
## YOB 1.091743 3.43406593 FALSE FALSE TRUE
## Q121011.fctr 2.049383 0.13736264 FALSE FALSE TRUE
## Q117186.fctr 3.433409 0.13736264 FALSE FALSE TRUE
## Q106997.fctr 6.438462 0.13736264 FALSE FALSE TRUE
## YOB.Age.dff 1.098214 0.86996337 FALSE FALSE TRUE
## Q108617.fctr 5.533742 0.13736264 FALSE FALSE TRUE
## Q98197.fctr 5.296178 0.13736264 FALSE FALSE TRUE
## Q106042.fctr 5.903915 0.13736264 FALSE FALSE TRUE
## Q115777.fctr 4.316667 0.13736264 FALSE FALSE TRUE
## Q123621.fctr 4.540059 0.13736264 FALSE FALSE TRUE
## Q106388.fctr 4.481283 0.13736264 FALSE FALSE TRUE
## Q114152.fctr 4.042929 0.13736264 FALSE FALSE TRUE
## Q124122.fctr 4.057292 0.13736264 FALSE FALSE TRUE
## Q120194.fctr 2.708163 0.13736264 FALSE FALSE TRUE
## Q116797.fctr 4.049738 0.13736264 FALSE FALSE TRUE
## Q105655.fctr 5.336634 0.13736264 FALSE FALSE TRUE
## Q115899.fctr 4.610619 0.13736264 FALSE FALSE TRUE
## Q116448.fctr 4.631420 0.13736264 FALSE FALSE TRUE
## Q117193.fctr 3.419355 0.13736264 FALSE FALSE TRUE
## Q108754.fctr 7.281746 0.13736264 FALSE FALSE TRUE
## Q108856.fctr 8.883721 0.13736264 FALSE FALSE TRUE
## YOB.Age.fctr 1.094828 0.41208791 FALSE FALSE TRUE
## Q123464.fctr 2.323988 0.13736264 FALSE FALSE TRUE
## Q99581.fctr 3.255578 0.13736264 FALSE FALSE TRUE
## Q114961.fctr 4.412791 0.13736264 FALSE FALSE TRUE
## Q104996.fctr 5.160256 0.13736264 FALSE FALSE TRUE
## Q108343.fctr 7.047244 0.13736264 FALSE FALSE TRUE
## Q120012.fctr 2.215613 0.13736264 FALSE FALSE TRUE
## Q120978.fctr 1.991394 0.13736264 FALSE FALSE TRUE
## .clusterid 1.239655 0.18315018 FALSE FALSE TRUE
## .clusterid.fctr 1.239655 0.18315018 FALSE FALSE TRUE
## Q98578.fctr 4.763689 0.13736264 FALSE FALSE TRUE
## Q103293.fctr 5.266234 0.13736264 FALSE FALSE TRUE
## Q106389.fctr 6.686275 0.13736264 FALSE FALSE TRUE
## Q98869.fctr 4.231552 0.13736264 FALSE FALSE TRUE
## Q112512.fctr 3.268994 0.13736264 FALSE FALSE TRUE
## Q116953.fctr 3.706444 0.13736264 FALSE FALSE TRUE
## Q100010.fctr 3.599558 0.13736264 FALSE FALSE TRUE
## Q111220.fctr 3.793269 0.13736264 FALSE FALSE TRUE
## Q102906.fctr 5.357827 0.13736264 FALSE FALSE TRUE
## Q121700.fctr 1.584833 0.13736264 FALSE FALSE TRUE
## Q112478.fctr 4.898507 0.13736264 FALSE FALSE TRUE
## Q115610.fctr 2.595819 0.13736264 FALSE FALSE TRUE
## Q119851.fctr 1.969543 0.13736264 FALSE FALSE TRUE
## Q114517.fctr 3.109244 0.13736264 FALSE FALSE TRUE
## Q118892.fctr 1.960591 0.13736264 FALSE FALSE TRUE
## Q115602.fctr 2.656420 0.13736264 FALSE FALSE TRUE
## Q120379.fctr 2.302326 0.13736264 FALSE FALSE TRUE
## Q107491.fctr 3.898383 0.13736264 FALSE FALSE TRUE
## Q114748.fctr 3.335664 0.13736264 FALSE FALSE TRUE
## Q99982.fctr 5.702703 0.13736264 FALSE FALSE TRUE
## Q113992.fctr 2.914172 0.13736264 FALSE FALSE TRUE
## Q115390.fctr 4.036176 0.13736264 FALSE FALSE TRUE
## Q118232.fctr 5.225490 0.13736264 FALSE FALSE TRUE
## Q96024.fctr 5.104938 0.13736264 FALSE FALSE TRUE
## Q115195.fctr 3.287912 0.13736264 FALSE FALSE FALSE
## Q121699.fctr 1.704385 0.13736264 FALSE FALSE FALSE
## Q100680.fctr 4.890533 0.13736264 FALSE FALSE FALSE
## Q111580.fctr 4.118863 0.13736264 FALSE FALSE FALSE
## Q102289.fctr 4.946429 0.13736264 FALSE FALSE FALSE
## Q102687.fctr 5.698246 0.13736264 FALSE FALSE FALSE
## Q105840.fctr 7.431034 0.13736264 FALSE FALSE FALSE
## Q101162.fctr 5.211838 0.13736264 FALSE FALSE FALSE
## Q108950.fctr 8.753488 0.13736264 FALSE FALSE FALSE
## Q116601.fctr 2.490694 0.13736264 FALSE FALSE FALSE
## Q108342.fctr 7.175299 0.13736264 FALSE FALSE FALSE
## Q100562.fctr 4.191919 0.13736264 FALSE FALSE FALSE
## Q113584.fctr 4.181058 0.13736264 FALSE FALSE FALSE
## Q109367.fctr 11.134503 0.13736264 FALSE FALSE FALSE
## Q99716.fctr 3.625821 0.13736264 FALSE FALSE FALSE
## Hhold.fctr 1.896030 0.32051282 FALSE FALSE FALSE
## Q112270.fctr 5.863799 0.13736264 FALSE FALSE FALSE
## Q98059.fctr 2.971805 0.13736264 FALSE FALSE FALSE
## Q111848.fctr 3.708229 0.13736264 FALSE FALSE FALSE
## Q102674.fctr 4.988166 0.13736264 FALSE FALSE FALSE
## Q114386.fctr 4.429395 0.13736264 FALSE FALSE FALSE
## Q98078.fctr 6.255556 0.13736264 FALSE FALSE FALSE
## Q102089.fctr 4.655271 0.13736264 FALSE FALSE FALSE
## Edn.fctr 1.103359 0.36630037 FALSE FALSE FALSE
## Q100689.fctr 4.553623 0.13736264 FALSE FALSE FALSE
## Q113583.fctr 3.138655 0.13736264 FALSE FALSE FALSE
## Q101163.fctr 6.202899 0.13736264 FALSE FALSE FALSE
## Q109244.fctr 0.000000 0.04578755 TRUE TRUE NA## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## Q109244.fctr NA 0 NA <NA> 0
## percentUnique zeroVar nzv is.cor.y.abs.low
## Q109244.fctr 0.04578755 TRUE TRUE NA## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "numeric data missing in : "
## YOB Party.fctr
## 239 547
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff
## 253
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Gender Income HouseholdStatus EducationLevel
## 88 665 316 521
## Party Q124742 Q124122 Q123464
## NA 2313 1954 1895
## Q123621 Q122769 Q122770 Q122771
## 1928 1855 1758 1745
## Q122120 Q121699 Q121700 Q120978
## 1719 1508 1543 1447
## Q121011 Q120379 Q120650 Q120472
## 1447 1485 1367 1504
## Q120194 Q120012 Q120014 Q119334
## 1657 1488 1641 1652
## Q119851 Q119650 Q118892 Q118117
## 1458 1522 1493 1635
## Q118232 Q118233 Q118237 Q117186
## 2006 1837 1789 1914
## Q117193 Q116797 Q116881 Q116953
## 1868 1939 1978 1953
## Q116601 Q116441 Q116448 Q116197
## 1847 1904 1927 1858
## Q115602 Q115777 Q115610 Q115611
## 1844 1943 1870 1754
## Q115899 Q115390 Q114961 Q114748
## 1956 1962 1905 1808
## Q115195 Q114517 Q114386 Q113992
## 1880 1870 1951 1849
## Q114152 Q113583 Q113584 Q113181
## 2031 1883 1901 1906
## Q112478 Q112512 Q112270 Q111848
## 2067 2004 2050 1872
## Q111580 Q111220 Q110740 Q109367
## 1993 1993 1949 2383
## Q108950 Q109244 Q108855 Q108617
## 2346 2731 2379 2265
## Q108856 Q108754 Q108342 Q108343
## 2388 2284 2262 2241
## Q107869 Q107491 Q106993 Q106997
## 2177 2125 2100 2113
## Q106272 Q106388 Q106389 Q106042
## 2084 2114 2140 2093
## Q105840 Q105655 Q104996 Q103293
## 2167 2042 2019 2042
## Q102906 Q102674 Q102687 Q102289
## 2116 2116 2038 2079
## Q102089 Q101162 Q101163 Q101596
## 2052 2094 2152 2104
## Q100689 Q100680 Q100562 Q99982
## 1969 2074 2080 2114
## Q100010 Q99716 Q99581 Q99480
## 2033 2071 2010 2016
## Q98869 Q98578 Q98059 Q98078
## 2088 2073 1984 2125
## Q98197 Q96024 .lcn
## 2084 2065 547## [1] "glb_feats_df:"## [1] 113 12## id exclude.as.feat rsp_var
## Party.fctr Party.fctr TRUE TRUE## id cor.y exclude.as.feat cor.y.abs cor.high.X
## USER_ID USER_ID 0.04126698 TRUE 0.04126698 <NA>
## Party.fctr Party.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## USER_ID 1 100 FALSE FALSE FALSE
## Party.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID <NA> NA FALSE TRUE
## Party.fctr <NA> NA NA NA
## rsp_var
## USER_ID NA
## Party.fctr TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end
## 15 select.features 7 0 0 288.391 291.195
## 16 fit.models 8 0 0 291.196 NA
## elapsed
## 15 2.804
## 16 NAStep 8.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 291.757 NA NA# load(paste0(glbOut$pfx, "dsk.RData"))
glbgetModelSelectFormula <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
glbgetDisplayModelsDf <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#glbgetDisplayModelsDf()
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor bgn end
## 1 fit.models_0_bgn 1 0 setup 291.757 291.791
## 2 fit.models_0_MFO 1 1 myMFO_classfr 291.791 NA
## elapsed
## 1 0.034
## 2 NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: MFO###myMFO_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.411000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] D R
## Levels: D R
## [1] "unique.prob:"
## y
## D R
## 0.5360825 0.4639175
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.904000 secs"
## parameter
## 1 none
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used## [1] "myfit_mdl: train diagnostics complete: 0.907000 secs"## Loading required namespace: pROC## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## D R
## 1 0.5360825 0.4639175
## 2 0.5360825 0.4639175
## 3 0.5360825 0.4639175
## 4 0.5360825 0.4639175
## 5 0.5360825 0.4639175
## 6 0.5360825 0.4639175
## Prediction
## Reference D R
## D 936 0
## R 810 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.360825e-01 0.000000e+00 5.123603e-01 5.596832e-01 5.360825e-01
## AccuracyPValue McnemarPValue
## 5.098012e-01 9.827871e-178
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## D R
## 1 0.5360825 0.4639175
## 2 0.5360825 0.4639175
## 3 0.5360825 0.4639175
## 4 0.5360825 0.4639175
## 5 0.5360825 0.4639175
## 6 0.5360825 0.4639175## Prediction
## Reference D R
## D 235 0
## R 203 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.365297e-01 0.000000e+00 4.885756e-01 5.839872e-01 5.365297e-01
## AccuracyPValue McnemarPValue
## 5.195666e-01 1.260484e-45
## [1] "myfit_mdl: predict complete: 6.293000 secs"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm 0 0.485
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.004 0.5 1 0
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.5360825
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5123603 0.5596832 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 1 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5365297
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4885756 0.5839872 0
## [1] "in MFO.Classifier$prob"
## D R
## 1 0.5360825 0.4639175
## 2 0.5360825 0.4639175
## 3 0.5360825 0.4639175
## 4 0.5360825 0.4639175
## 5 0.5360825 0.4639175
## 6 0.5360825 0.4639175
## [1] "myfit_mdl: exit: 6.338000 secs"
## label step_major step_minor label_minor bgn
## 2 fit.models_0_MFO 1 1 myMFO_classfr 291.791
## 3 fit.models_0_Random 1 2 myrandom_classfr 298.136
## end elapsed
## 2 298.136 6.345
## 3 NA NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Random###myrandom_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.403000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.789000 secs"
## parameter
## 1 none
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used
## [1] "myfit_mdl: train diagnostics complete: 0.791000 secs"
## [1] "in Random.Classifier$prob"
## Prediction
## Reference D R
## D 936 0
## R 810 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.360825e-01 0.000000e+00 5.123603e-01 5.596832e-01 5.360825e-01
## AccuracyPValue McnemarPValue
## 5.098012e-01 9.827871e-178
## [1] "in Random.Classifier$prob"
## Prediction
## Reference D R
## D 235 0
## R 203 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.365297e-01 0.000000e+00 4.885756e-01 5.839872e-01 5.365297e-01
## AccuracyPValue McnemarPValue
## 5.195666e-01 1.260484e-45
## [1] "myfit_mdl: predict complete: 6.702000 secs"
## id feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.381 0.002 0.4916667
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5277778 0.4555556 0.5138295 0.55
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.5360825 0.5123603
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5596832 0 0.5576145 0.612766
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.5024631 0.5042029 0.55 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5365297 0.4885756 0.5839872
## max.Kappa.OOB
## 1 0
## [1] "in Random.Classifier$prob"
## [1] "myfit_mdl: exit: 7.419000 secs"# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## bgn end elapsed
## 3 298.136 305.567 7.431
## 4 305.568 NA NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: Gender.fctr,Q101163.fctr"
## [1] "myfit_mdl: setup complete: 0.671000 secs"## Loading required package: glmnet## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
##
## expand## Loaded glmnet 2.0-5## Fitting alpha = 0.1, lambda = 0.000983 on full training set
## [1] "myfit_mdl: train complete: 1.387000 secs"
## alpha lambda
## 1 0.1 0.0009825205
## Length Class Mode
## a0 45 -none- numeric
## beta 180 dgCMatrix S4
## df 45 -none- numeric
## dim 2 -none- numeric
## lambda 45 -none- numeric
## dev.ratio 45 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 4 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF Gender.fctrM Q101163.fctrDad
## -0.31106828 -0.00973240 0.36620048 0.09363164
## Q101163.fctrMom
## -0.60199303
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "Gender.fctrF" "Gender.fctrM" "Q101163.fctrDad"
## [5] "Q101163.fctrMom"
## [1] "myfit_mdl: train diagnostics complete: 1.494000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.5557481 0.5595647
## 11 0.50 0.5557481 0.5595647
## Prediction
## Reference D R
## D 496 440
## R 329 481
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.595647e-01 1.226108e-01 5.359087e-01 5.830202e-01 5.360825e-01
## AccuracyPValue McnemarPValue
## 2.586206e-02 7.287418e-05
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.5421412 0.5410959
## 11 0.50 0.5421412 0.5410959
## Prediction
## Reference D R
## D 118 117
## R 84 119
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.54109589 0.08736757 0.49314387 0.58848926 0.53652968
## AccuracyPValue McnemarPValue
## 0.44331935 0.02400145
## [1] "myfit_mdl: predict complete: 6.789000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Gender.fctr,Q101163.fctr 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.71 0.025 0.5618708
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5299145 0.5938272 0.5720013 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.5557481 0.5595647 0.5359087
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5830202 0.1226108 0.5441673 0.5021277
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.5862069 0.5584215 0.5 0.5421412
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5410959 0.4931439 0.5884893
## max.Kappa.OOB
## 1 0.08736757
## [1] "myfit_mdl: exit: 6.854000 secs"if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: Gender.fctr,Q101163.fctr"
## [1] "myfit_mdl: setup complete: 0.673000 secs"## Loading required package: rpart## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00633 on full training set
## [1] "myfit_mdl: train complete: 2.143000 secs"## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1746
##
## CP nsplit rel error
## 1 0.02530864 0 1.0000000
## 2 0.00000000 2 0.9493827
##
## Variable importance
## Gender.fctrM Gender.fctrF Q101163.fctrMom
## 39 36 26
##
## Node number 1: 1746 observations, complexity param=0.02530864
## predicted class=D expected loss=0.4639175 P(node) =1
## class counts: 936 810
## probabilities: 0.536 0.464
## left son=2 (738 obs) right son=3 (1008 obs)
## Primary splits:
## Gender.fctrM < 0.5 to the left, improve=8.4274760, (0 missing)
## Q101163.fctrMom < 0.5 to the right, improve=7.4217640, (0 missing)
## Gender.fctrF < 0.5 to the right, improve=7.1271380, (0 missing)
## Q101163.fctrDad < 0.5 to the left, improve=0.7371618, (0 missing)
## Surrogate splits:
## Gender.fctrF < 0.5 to the right, agree=0.967, adj=0.921, (0 split)
##
## Node number 2: 738 observations
## predicted class=D expected loss=0.4065041 P(node) =0.4226804
## class counts: 438 300
## probabilities: 0.593 0.407
##
## Node number 3: 1008 observations, complexity param=0.02530864
## predicted class=R expected loss=0.4940476 P(node) =0.5773196
## class counts: 498 510
## probabilities: 0.494 0.506
## left son=6 (87 obs) right son=7 (921 obs)
## Primary splits:
## Q101163.fctrMom < 0.5 to the right, improve=5.674500, (0 missing)
## Q101163.fctrDad < 0.5 to the left, improve=1.045878, (0 missing)
##
## Node number 6: 87 observations
## predicted class=D expected loss=0.3333333 P(node) =0.04982818
## class counts: 58 29
## probabilities: 0.667 0.333
##
## Node number 7: 921 observations
## predicted class=R expected loss=0.4777416 P(node) =0.5274914
## class counts: 440 481
## probabilities: 0.478 0.522
##
## n= 1746
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1746 810 D (0.5360825 0.4639175)
## 2) Gender.fctrM< 0.5 738 300 D (0.5934959 0.4065041) *
## 3) Gender.fctrM>=0.5 1008 498 R (0.4940476 0.5059524)
## 6) Q101163.fctrMom>=0.5 87 29 D (0.6666667 0.3333333) *
## 7) Q101163.fctrMom< 0.5 921 440 R (0.4777416 0.5222584) *
## [1] "myfit_mdl: train diagnostics complete: 2.943000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.5557481 0.5595647
## 11 0.50 0.5557481 0.5595647
## Prediction
## Reference D R
## D 496 440
## R 329 481
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.595647e-01 1.226108e-01 5.359087e-01 5.830202e-01 5.360825e-01
## AccuracyPValue McnemarPValue
## 2.586206e-02 7.287418e-05
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.5421412 0.5410959
## 11 0.50 0.5421412 0.5410959
## Prediction
## Reference D R
## D 118 117
## R 84 119
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.54109589 0.08736757 0.49314387 0.58848926 0.53652968
## AccuracyPValue McnemarPValue
## 0.44331935 0.02400145
## [1] "myfit_mdl: predict complete: 8.373000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Gender.fctr,Q101163.fctr 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.464 0.011 0.5618708
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5299145 0.5938272 0.5649691 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.5557481 0.5595647 0.5359087
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5830202 0.1225971 0.5441673 0.5021277
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.5862069 0.544859 0.5 0.5421412
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5410959 0.4931439 0.5884893
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.08736757 0.01648052 0.03319541
## [1] "myfit_mdl: exit: 8.432000 secs"if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
} ## label step_major step_minor label_minor
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## bgn end elapsed
## 4 305.568 320.895 15.327
## 5 320.896 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q101163.fctr,Gender.fctr:Q100562.fctr,Gender.fctr:Q102674.fctr,Gender.fctr:Q100689.fctr,Gender.fctr:Q113583.fctr,Gender.fctr:Q98078.fctr"
## [1] "myfit_mdl: setup complete: 0.693000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0212 on full training set
## [1] "myfit_mdl: train complete: 3.195000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 61 -none- numeric
## beta 2074 dgCMatrix S4
## df 61 -none- numeric
## dim 2 -none- numeric
## lambda 61 -none- numeric
## dev.ratio 61 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 34 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrF
## -0.23569103 -0.02463720
## Gender.fctrM Q101163.fctrDad
## 0.28604244 0.09092952
## Q101163.fctrMom Gender.fctrF:Q100562.fctrNo
## -0.46658238 0.19467105
## Gender.fctrM:Q100562.fctrNo Gender.fctrN:Q100689.fctrNo
## 0.33808832 -0.17645536
## Gender.fctrM:Q100689.fctrYes Gender.fctrM:Q102674.fctrNo
## -0.23431799 0.14229138
## Gender.fctrF:Q102674.fctrYes Gender.fctrM:Q113583.fctrTalk
## -0.33309117 0.09379886
## Gender.fctrF:Q113583.fctrTunes Gender.fctrM:Q113583.fctrTunes
## -0.02602276 -0.24551432
## Gender.fctrN:Q98078.fctrNo Gender.fctrM:Q98078.fctrNo
## -0.60124013 -0.06487166
## Gender.fctrN:Q98078.fctrYes Gender.fctrF:Q98078.fctrYes
## 0.36450799 -0.16051393
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrF
## -0.23955407 -0.02251079
## Gender.fctrM Q101163.fctrDad
## 0.29480400 0.10349126
## Q101163.fctrMom Gender.fctrF:Q100562.fctrNo
## -0.47271466 0.21871570
## Gender.fctrM:Q100562.fctrNo Gender.fctrN:Q100689.fctrNo
## 0.35552549 -0.28822233
## Gender.fctrM:Q100689.fctrYes Gender.fctrM:Q102674.fctrNo
## -0.25473783 0.15325516
## Gender.fctrF:Q102674.fctrYes Gender.fctrM:Q113583.fctrTalk
## -0.35093199 0.09868848
## Gender.fctrF:Q113583.fctrTunes Gender.fctrM:Q113583.fctrTunes
## -0.03103399 -0.25930572
## Gender.fctrN:Q98078.fctrNo Gender.fctrM:Q98078.fctrNo
## -0.65309190 -0.07788385
## Gender.fctrN:Q98078.fctrYes Gender.fctrF:Q98078.fctrYes
## 0.42921693 -0.17363486
## [1] "myfit_mdl: train diagnostics complete: 3.827000 secs"
## Prediction
## Reference D R
## D 614 322
## R 407 403
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.824742e-01 1.546058e-01 5.589337e-01 6.057371e-01 5.360825e-01
## AccuracyPValue McnemarPValue
## 5.357338e-05 1.863848e-03
## Prediction
## Reference D R
## D 152 83
## R 107 96
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5662100 0.1206864 0.5183397 0.6131802 0.5365297
## AccuracyPValue McnemarPValue
## 0.1153889 0.0951976
## [1] "myfit_mdl: predict complete: 9.390000 secs"
## id
## 1 Interact.High.cor.Y##rcv#glmnet
## feats
## 1 Gender.fctr,Q101163.fctr,Gender.fctr:Q100562.fctr,Gender.fctr:Q102674.fctr,Gender.fctr:Q100689.fctr,Gender.fctr:Q113583.fctr,Gender.fctr:Q98078.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.492 0.099
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5767569 0.6559829 0.4975309 0.6023801
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5250814 0.5578465
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5589337 0.6057371 0.1056567
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5598575 0.6468085 0.4729064 0.5851273
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.5026178 0.56621
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5183397 0.6131802 0.1206864
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02368393 0.04801874
## [1] "myfit_mdl: exit: 9.470000 secs"# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## 6 fit.models_0_Low.cor.X 1 5 glmnet
## bgn end elapsed
## 5 320.896 330.381 9.485
## 6 330.381 NA NAindepVar <- mygetIndepVar(glb_feats_df)
indepVar <- setdiff(indepVar, unique(glb_feats_df$cor.high.X))
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q114386.fctr,Q102089.fctr,Edn.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.687000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.0247 on full training set
## [1] "myfit_mdl: train complete: 7.359000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 66 -none- numeric
## beta 15906 dgCMatrix S4
## df 66 -none- numeric
## dim 2 -none- numeric
## lambda 66 -none- numeric
## dev.ratio 66 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 241 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.L
## -0.23846652 -0.03304072
## Edn.fctr^4 Gender.fctrM
## 0.05128366 0.21492884
## Hhold.fctrPKy Q101163.fctrMom
## 0.27426485 -0.31022847
## Q104996.fctrNo Q106389.fctrNo
## 0.01753369 0.13654651
## Q108950.fctrRisk-friendly Q113181.fctrNo
## -0.08072459 -0.07404136
## Q113181.fctrYes Q115195.fctrNo
## 0.19290110 0.01918436
## Q115611.fctrNo Q115611.fctrYes
## -0.18111495 0.27929364
## Q116441.fctrNo Q116441.fctrYes
## -0.04718884 0.11374224
## Q116601.fctrNo Q119851.fctrNo
## 0.29924654 0.03449318
## Q120379.fctrYes Q120650.fctrNo
## -0.06195411 -0.10688317
## Q98197.fctrNo Q98197.fctrYes
## -0.32696152 0.06474982
## Q98869.fctrNo Q99480.fctrYes
## -0.12408063 0.04903837
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.L
## -0.2486882957 -0.0490062685
## Edn.fctr^4 Gender.fctrM
## 0.0638720221 0.2296986941
## Hhold.fctrPKy Q101163.fctrMom
## 0.3425353890 -0.3424319842
## Q104996.fctrNo Q106388.fctrNo
## 0.0437966880 0.0050973617
## Q106389.fctrNo Q108950.fctrRisk-friendly
## 0.1559905300 -0.1207488929
## Q113181.fctrNo Q113181.fctrYes
## -0.0859745166 0.2039395179
## Q114386.fctrTMI Q115195.fctrNo
## -0.0195365274 0.0416692306
## Q115611.fctrNo Q115611.fctrYes
## -0.1904803883 0.2883793596
## Q116441.fctrNo Q116441.fctrYes
## -0.0699604558 0.1219971884
## Q116601.fctrNo Q118892.fctrYes
## 0.3360681100 -0.0055063248
## Q119851.fctrNo Q119851.fctrYes
## 0.0525925546 -0.0006243739
## Q120379.fctrYes Q120472.fctrScience
## -0.0780770859 0.0033689973
## Q120650.fctrNo Q122771.fctrPt
## -0.1378760071 0.0255145580
## Q98197.fctrNo Q98197.fctrYes
## -0.3504038475 0.0683594644
## Q98869.fctrNo Q99480.fctrYes
## -0.1515925410 0.0729293498
## [1] "myfit_mdl: train diagnostics complete: 8.022000 secs"
## Prediction
## Reference D R
## D 509 427
## R 251 559
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.116838e-01 2.305550e-01 5.883650e-01 6.346266e-01 5.360825e-01
## AccuracyPValue McnemarPValue
## 1.110403e-10 1.806867e-11
## Prediction
## Reference D R
## D 182 53
## R 134 69
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.730594e-01 1.175634e-01 5.252321e-01 6.198932e-01 5.365297e-01
## AccuracyPValue McnemarPValue
## 6.851176e-02 4.910431e-09
## [1] "myfit_mdl: predict complete: 16.646000 secs"
## id
## 1 Low.cor.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q114386.fctr,Q102089.fctr,Edn.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 6.595 0.596
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5945869 0.8076923 0.3814815 0.6635631
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.45 0.6224944 0.5631921
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.588365 0.6346266 0.1014582
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5571848 0.7744681 0.3399015 0.6077246
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.4246154 0.5730594
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5252321 0.6198932 0.1175634
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0182199 0.03682851
## [1] "myfit_mdl: exit: 16.908000 secs"fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 6 fit.models_0_Low.cor.X 1 5 glmnet 330.381 347.326
## 7 fit.models_0_end 1 6 teardown 347.326 NA
## elapsed
## 6 16.945
## 7 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 16 fit.models 8 0 0 291.196 347.339 56.143
## 17 fit.models 8 1 1 347.340 NA NA```{r fit.models_1, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 352.06 NA NA## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 352.060 352.073
## 2 fit.models_1_All.X 1 1 setup 352.073 NA
## elapsed
## 1 0.013
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 352.073 352.081
## 3 fit.models_1_All.X 1 2 glmnet 352.081 NA
## elapsed
## 2 0.008
## 3 NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q100562.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q102674.fctr,Q114386.fctr,Q98078.fctr,Q102089.fctr,Edn.fctr,Q100689.fctr,Q113583.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.670000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0247 on full training set
## [1] "myfit_mdl: train complete: 8.052000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 74 -none- numeric
## beta 18574 dgCMatrix S4
## df 74 -none- numeric
## dim 2 -none- numeric
## lambda 74 -none- numeric
## dev.ratio 74 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 251 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) .rnorm
## -0.2024436525 0.0111851436
## Edn.fctr.L Edn.fctr.Q
## -0.0442974895 0.1947341846
## Edn.fctr.C Edn.fctr^4
## 0.0570175265 0.2729610307
## Edn.fctr^5 Edn.fctr^6
## 0.1346178755 -0.1145427380
## Edn.fctr^7 Gender.fctrF
## -0.1078588137 -0.0594976461
## Gender.fctrM Hhold.fctrMKn
## 0.2541399629 -0.0301668970
## Hhold.fctrMKy Hhold.fctrPKn
## 0.1228855404 -0.1263731668
## Hhold.fctrPKy Hhold.fctrSKn
## 0.8227048855 -0.1017133418
## Income.fctr.Q Income.fctr.C
## -0.0368056668 0.2164636280
## Income.fctr^4 Income.fctr^6
## 0.1609350877 0.0977268563
## Q100010.fctrNo Q100562.fctrNo
## -0.2394966354 0.4606105816
## Q100680.fctrNo Q100689.fctrNo
## -0.1709471157 -0.0513563381
## Q100689.fctrYes Q101162.fctrOptimist
## -0.4103754929 -0.1197022400
## Q101162.fctrPessimist Q101163.fctrDad
## 0.2479866955 0.1128744600
## Q101163.fctrMom Q101596.fctrNo
## -0.4836306838 0.0924245633
## Q101596.fctrYes Q102089.fctrOwn
## 0.3891077764 -0.1970036403
## Q102089.fctrRent Q102289.fctrYes
## -0.2712788062 -0.1376479461
## Q102674.fctrNo Q102674.fctrYes
## 0.1525589894 -0.3468990160
## Q102687.fctrNo Q102687.fctrYes
## 0.0452190915 -0.0752103492
## Q102906.fctrYes Q103293.fctrNo
## 0.0412085832 0.0706766536
## Q104996.fctrNo Q104996.fctrYes
## 0.2139199390 -0.0703703021
## Q105840.fctrNo Q106042.fctrNo
## 0.0565600386 0.1116920002
## Q106042.fctrYes Q106272.fctrNo
## 0.0435874318 0.1311109254
## Q106272.fctrYes Q106388.fctrNo
## 0.0931633962 0.2348810419
## Q106388.fctrYes Q106389.fctrNo
## -0.1555239827 0.2506641297
## Q106389.fctrYes Q106993.fctrNo
## -0.2124741176 0.0984012281
## Q106997.fctrGr Q107491.fctrNo
## -0.0191634978 0.1270529594
## Q107491.fctrYes Q107869.fctrYes
## -0.1996119277 0.0438806922
## Q108342.fctrIn-person Q108342.fctrOnline
## -0.0754977713 -0.2120492282
## Q108343.fctrYes Q108617.fctrNo
## -0.0408147050 0.1972716556
## Q108855.fctrUmm... Q108856.fctrSocialize
## 0.2459122687 0.3563008371
## Q108856.fctrSpace Q108950.fctrCautious
## -0.0851883209 0.0184484231
## Q108950.fctrRisk-friendly Q109367.fctrNo
## -0.4519724545 -0.2446126162
## Q109367.fctrYes Q110740.fctrMac
## -0.1397985033 0.1611789305
## Q110740.fctrPC Q111580.fctrSupportive
## 0.2407760027 -0.0241735616
## Q111848.fctrNo Q111848.fctrYes
## -0.1607959512 -0.0628321292
## Q112270.fctrNo Q112270.fctrYes
## -0.0754607028 -0.2790605626
## Q112478.fctrNo Q112512.fctrNo
## 0.3082548243 0.1892691776
## Q112512.fctrYes Q113181.fctrNo
## 0.0029687606 -0.0846028278
## Q113181.fctrYes Q113583.fctrTunes
## 0.4419331612 -0.3278277496
## Q113584.fctrPeople Q113992.fctrNo
## -0.0525650826 0.0016350373
## Q113992.fctrYes Q114152.fctrYes
## -0.0451076804 -0.0184985110
## Q114386.fctrMysterious Q114386.fctrTMI
## 0.1576222540 -0.1005252524
## Q114517.fctrNo Q114748.fctrNo
## -0.0877373081 -0.0324232246
## Q114961.fctrNo Q114961.fctrYes
## -0.0457356207 0.1315039221
## Q115195.fctrNo Q115195.fctrYes
## 0.1545806451 -0.1992718748
## Q115390.fctrNo Q115602.fctrNo
## 0.2923707845 -0.0580081240
## Q115610.fctrNo Q115610.fctrYes
## -0.1781874528 0.0085526779
## Q115611.fctrNo Q115611.fctrYes
## -0.2178168507 0.4489561144
## Q115777.fctrEnd Q115777.fctrStart
## -0.1582238319 0.0954546193
## Q115899.fctrCs Q115899.fctrMe
## -0.2413296674 0.0524273779
## Q116197.fctrP.M. Q116441.fctrNo
## 0.1743847518 -0.2579497589
## Q116441.fctrYes Q116448.fctrNo
## 0.2741072149 0.1370455971
## Q116448.fctrYes Q116601.fctrNo
## 0.0186617261 0.4033024656
## Q116601.fctrYes Q116797.fctrNo
## -0.1515105731 0.1935195280
## Q116797.fctrYes Q116881.fctrRight
## 0.0880484135 0.0926906341
## Q116953.fctrNo Q116953.fctrYes
## -0.0840155376 -0.1234424053
## Q117186.fctrCool headed Q117186.fctrHot headed
## -0.1482911575 0.0942153297
## Q117193.fctrOdd hours Q117193.fctrStandard hours
## 0.0002571871 0.0132123662
## Q118117.fctrYes Q118232.fctrId
## 0.1360914380 -0.0983902752
## Q118232.fctrPr Q118233.fctrNo
## -0.1858983740 0.0770490508
## Q118237.fctrNo Q118892.fctrNo
## 0.0227463223 0.0269276019
## Q118892.fctrYes Q119334.fctrNo
## -0.1352810482 -0.0443721479
## Q119334.fctrYes Q119650.fctrGiving
## 0.0675175606 0.0577118743
## Q119650.fctrReceiving Q119851.fctrNo
## 0.0588557694 0.1693567303
## Q119851.fctrYes Q120012.fctrNo
## -0.0635863527 0.1094341843
## Q120014.fctrNo Q120014.fctrYes
## -0.1455800740 -0.0369657862
## Q120194.fctrStudy first Q120379.fctrNo
## -0.2048195755 0.0934504026
## Q120379.fctrYes Q120472.fctrScience
## -0.2783381906 0.1502910561
## Q120650.fctrNo Q120650.fctrYes
## -0.3740818549 0.1219197129
## Q120978.fctrYes Q121011.fctrNo
## -0.1257172960 0.1155251469
## Q121699.fctrNo Q121699.fctrYes
## -0.0664059892 -0.1176807904
## Q121700.fctrNo Q121700.fctrYes
## -0.0074204904 -0.1297393825
## Q122120.fctrYes Q122770.fctrNo
## 0.2024255717 -0.0592128862
## Q122771.fctrPc Q122771.fctrPt
## -0.0236138603 0.3154235248
## Q123464.fctrYes Q123621.fctrNo
## -0.4770201306 0.1091989131
## Q124122.fctrNo Q124122.fctrYes
## 0.0653857387 -0.0175577325
## Q124742.fctrYes Q96024.fctrNo
## 0.0676186221 -0.0380344967
## Q96024.fctrYes Q98059.fctrOnly-child
## 0.0838135457 0.3365998906
## Q98059.fctrYes Q98078.fctrNo
## -0.0523795180 -0.0208181384
## Q98197.fctrNo Q98197.fctrYes
## -0.5338590470 0.2015399920
## Q98578.fctrNo Q98578.fctrYes
## 0.1537791359 0.1724820483
## Q98869.fctrNo Q99480.fctrNo
## -0.4069262486 -0.1623722608
## Q99480.fctrYes Q99581.fctrYes
## 0.3669223177 0.0568685628
## Q99716.fctrNo Q99982.fctrCheck!
## -0.1821926834 0.0762862969
## YOB.Age.fctr.L YOB.Age.fctr.Q
## -0.3071663891 -0.2647232726
## YOB.Age.fctr.C YOB.Age.fctr^4
## -0.1072842886 -0.0524644398
## YOB.Age.fctr^5 YOB.Age.fctr^7
## -0.0269230656 0.1194405569
## YOB.Age.fctr^8 Q109244.fctrNA:.clusterid.fctr2
## 0.0857630594 -0.0111457076
## Q109244.fctrNA:.clusterid.fctr3 YOB.Age.fctr(15,20]:YOB.Age.dff
## 0.0529849037 -0.0095520713
## YOB.Age.fctr(20,25]:YOB.Age.dff YOB.Age.fctr(30,35]:YOB.Age.dff
## 0.0380836715 0.0007277624
## YOB.Age.fctr(35,40]:YOB.Age.dff YOB.Age.fctr(50,65]:YOB.Age.dff
## -0.0559077872 0.0011994690
## [1] "max lambda < lambdaOpt:"
## (Intercept) .rnorm
## -0.2042215154 0.0110033826
## Edn.fctr.L Edn.fctr.Q
## -0.0407535860 0.2079840401
## Edn.fctr.C Edn.fctr^4
## 0.0689773125 0.2826726986
## Edn.fctr^5 Edn.fctr^6
## 0.1464772124 -0.1183788701
## Edn.fctr^7 Gender.fctrF
## -0.1128134156 -0.0569927606
## Gender.fctrM Hhold.fctrMKn
## 0.2613700236 -0.0340465279
## Hhold.fctrMKy Hhold.fctrPKn
## 0.1250932456 -0.1324040847
## Hhold.fctrPKy Hhold.fctrSKn
## 0.8427118661 -0.1065239644
## Income.fctr.Q Income.fctr.C
## -0.0433564845 0.2245743194
## Income.fctr^4 Income.fctr^6
## 0.1690514741 0.1035263211
## Q100010.fctrNo Q100562.fctrNo
## -0.2497298160 0.4822464108
## Q100680.fctrNo Q100689.fctrNo
## -0.1807830733 -0.0647345148
## Q100689.fctrYes Q101162.fctrOptimist
## -0.4354990734 -0.1251457575
## Q101162.fctrPessimist Q101163.fctrDad
## 0.2573593714 0.1172833908
## Q101163.fctrMom Q101596.fctrNo
## -0.4945961607 0.1087885277
## Q101596.fctrYes Q102089.fctrOwn
## 0.4128297303 -0.2147991379
## Q102089.fctrRent Q102289.fctrNo
## -0.2919544016 0.0001903800
## Q102289.fctrYes Q102674.fctrNo
## -0.1454758031 0.1577531073
## Q102674.fctrYes Q102687.fctrNo
## -0.3575013519 0.0555808641
## Q102687.fctrYes Q102906.fctrYes
## -0.0767038930 0.0470276115
## Q103293.fctrNo Q104996.fctrNo
## 0.0781027425 0.2166326190
## Q104996.fctrYes Q105840.fctrNo
## -0.0773991883 0.0566444393
## Q106042.fctrNo Q106042.fctrYes
## 0.1221072904 0.0505755621
## Q106272.fctrNo Q106272.fctrYes
## 0.1430131668 0.1034509121
## Q106388.fctrNo Q106388.fctrYes
## 0.2423010549 -0.1649205548
## Q106389.fctrNo Q106389.fctrYes
## 0.2497812152 -0.2324070448
## Q106993.fctrNo Q106997.fctrGr
## 0.1060524232 -0.0256856757
## Q107491.fctrNo Q107491.fctrYes
## 0.1283373652 -0.2132433214
## Q107869.fctrYes Q108342.fctrIn-person
## 0.0554856661 -0.0845621306
## Q108342.fctrOnline Q108343.fctrYes
## -0.2249534075 -0.0461991115
## Q108617.fctrNo Q108855.fctrUmm...
## 0.2112492992 0.2597989499
## Q108856.fctrSocialize Q108856.fctrSpace
## 0.3726724796 -0.0934508530
## Q108950.fctrCautious Q108950.fctrRisk-friendly
## 0.0209520003 -0.4618005866
## Q109367.fctrNo Q109367.fctrYes
## -0.2612690315 -0.1464939533
## Q110740.fctrMac Q110740.fctrPC
## 0.1738408055 0.2542117819
## Q111580.fctrSupportive Q111848.fctrNo
## -0.0262842836 -0.1709111869
## Q111848.fctrYes Q112270.fctrNo
## -0.0685689192 -0.0876659551
## Q112270.fctrYes Q112478.fctrNo
## -0.2939542323 0.3188201052
## Q112512.fctrNo Q112512.fctrYes
## 0.2055846712 0.0153057985
## Q113181.fctrNo Q113181.fctrYes
## -0.0859642777 0.4543692335
## Q113583.fctrTunes Q113584.fctrPeople
## -0.3388685080 -0.0599830235
## Q113992.fctrNo Q113992.fctrYes
## 0.0008891958 -0.0491056144
## Q114152.fctrYes Q114386.fctrMysterious
## -0.0265516379 0.1644845244
## Q114386.fctrTMI Q114517.fctrNo
## -0.1016342007 -0.0946559549
## Q114748.fctrNo Q114961.fctrNo
## -0.0384019420 -0.0477093360
## Q114961.fctrYes Q115195.fctrNo
## 0.1431035859 0.1557333819
## Q115195.fctrYes Q115390.fctrNo
## -0.2123021468 0.3027408691
## Q115602.fctrNo Q115610.fctrNo
## -0.0640768047 -0.1784536320
## Q115610.fctrYes Q115611.fctrNo
## 0.0174654126 -0.2247446841
## Q115611.fctrYes Q115777.fctrEnd
## 0.4576956505 -0.1673792971
## Q115777.fctrStart Q115899.fctrCs
## 0.0973576291 -0.2523686313
## Q115899.fctrMe Q116197.fctrA.M.
## 0.0543933192 0.0049360502
## Q116197.fctrP.M. Q116441.fctrNo
## 0.1843352482 -0.2705357971
## Q116441.fctrYes Q116448.fctrNo
## 0.2729089647 0.1479006520
## Q116448.fctrYes Q116601.fctrNo
## 0.0284532479 0.4042380994
## Q116601.fctrYes Q116797.fctrNo
## -0.1624763418 0.2119695928
## Q116797.fctrYes Q116881.fctrRight
## 0.1026620688 0.0954780346
## Q116953.fctrNo Q116953.fctrYes
## -0.1051046755 -0.1408018255
## Q117186.fctrCool headed Q117186.fctrHot headed
## -0.1599530380 0.0933606533
## Q117193.fctrOdd hours Q117193.fctrStandard hours
## 0.0101251009 0.0248498133
## Q118117.fctrYes Q118232.fctrId
## 0.1411799747 -0.1097789192
## Q118232.fctrPr Q118233.fctrNo
## -0.1952338167 0.0853911725
## Q118237.fctrNo Q118892.fctrNo
## 0.0249080102 0.0264262118
## Q118892.fctrYes Q119334.fctrNo
## -0.1407485238 -0.0494285992
## Q119334.fctrYes Q119650.fctrGiving
## 0.0703300486 0.0686769745
## Q119650.fctrReceiving Q119851.fctrNo
## 0.0706271999 0.1751164459
## Q119851.fctrYes Q120012.fctrNo
## -0.0627341858 0.1145143639
## Q120014.fctrNo Q120014.fctrYes
## -0.1549489773 -0.0459374164
## Q120194.fctrStudy first Q120379.fctrNo
## -0.2134879405 0.0967227141
## Q120379.fctrYes Q120472.fctrScience
## -0.2857107062 0.1559831300
## Q120650.fctrNo Q120650.fctrYes
## -0.3796161377 0.1298048266
## Q120978.fctrYes Q121011.fctrNo
## -0.1290096303 0.1207855872
## Q121699.fctrNo Q121699.fctrYes
## -0.0767141039 -0.1246031523
## Q121700.fctrNo Q121700.fctrYes
## -0.0113964002 -0.1363632464
## Q122120.fctrYes Q122770.fctrNo
## 0.2129454216 -0.0642135394
## Q122771.fctrPc Q122771.fctrPt
## -0.0239283208 0.3257289966
## Q123464.fctrYes Q123621.fctrNo
## -0.4927230091 0.1193495826
## Q124122.fctrNo Q124122.fctrYes
## 0.0672896213 -0.0197566114
## Q124742.fctrYes Q96024.fctrNo
## 0.0715148917 -0.0378783761
## Q96024.fctrYes Q98059.fctrOnly-child
## 0.0892791856 0.3541652414
## Q98059.fctrYes Q98078.fctrNo
## -0.0535209754 -0.0285855529
## Q98197.fctrNo Q98197.fctrYes
## -0.5484798718 0.2004404463
## Q98578.fctrNo Q98578.fctrYes
## 0.1714068228 0.1946642199
## Q98869.fctrNo Q98869.fctrYes
## -0.4198941963 -0.0086465251
## Q99480.fctrNo Q99480.fctrYes
## -0.1589510915 0.3838616651
## Q99581.fctrYes Q99716.fctrNo
## 0.0657754933 -0.1971125317
## Q99982.fctrCheck! YOB.Age.fctr.L
## 0.0856839080 -0.3267835649
## YOB.Age.fctr.Q YOB.Age.fctr.C
## -0.2768584499 -0.1165077129
## YOB.Age.fctr^4 YOB.Age.fctr^5
## -0.0636431883 -0.0281297225
## YOB.Age.fctr^7 YOB.Age.fctr^8
## 0.1326176210 0.0854443056
## Q109244.fctrNA:.clusterid.fctr2 Q109244.fctrNA:.clusterid.fctr3
## -0.0190978510 0.0643226511
## YOB.Age.fctr(15,20]:YOB.Age.dff YOB.Age.fctr(20,25]:YOB.Age.dff
## -0.0130045917 0.0406687854
## YOB.Age.fctr(30,35]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.0025499774 -0.0564718198
## YOB.Age.fctr(50,65]:YOB.Age.dff
## 0.0020418886
## [1] "myfit_mdl: train diagnostics complete: 8.690000 secs"
## Prediction
## Reference D R
## D 595 341
## R 208 602
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.855670e-01 3.747520e-01 6.632044e-01 7.073043e-01 5.360825e-01
## AccuracyPValue McnemarPValue
## 3.240583e-37 1.764628e-08
## Prediction
## Reference D R
## D 112 123
## R 58 145
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.867580e-01 1.868089e-01 5.390441e-01 6.332921e-01 5.365297e-01
## AccuracyPValue McnemarPValue
## 1.945046e-02 1.964109e-06
## [1] "myfit_mdl: predict complete: 17.669000 secs"
## id
## 1 All.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q100562.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q102674.fctr,Q114386.fctr,Q98078.fctr,Q102089.fctr,Edn.fctr,Q100689.fctr,Q113583.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 7.303 0.691
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6789055 0.7553419 0.6024691 0.7573718
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.45 0.6868226 0.5689194
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6632044 0.7073043 0.1280509
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5683681 0.6638298 0.4729064 0.6153233
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.6157113 0.586758
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5390441 0.6332921 0.1868089
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01451469 0.02957011
## [1] "myfit_mdl: exit: 17.938000 secs"## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 352.081 370.044
## 4 fit.models_1_preProc 1 3 preProc 370.045 NA
## elapsed
## 3 17.963
## 4 NA## min.elapsedtime.everything
## Random###myrandom_classfr 0.381
## MFO###myMFO_classfr 0.485
## Max.cor.Y.rcv.1X1###glmnet 0.710
## Max.cor.Y##rcv#rpart 1.464
## Interact.High.cor.Y##rcv#glmnet 2.492
## Low.cor.X##rcv#glmnet 6.595
## All.X##rcv#glmnet 7.303## label step_major step_minor label_minor bgn end
## 4 fit.models_1_preProc 1 3 preProc 370.045 370.09
## 5 fit.models_1_end 1 4 teardown 370.091 NA
## elapsed
## 4 0.045
## 5 NA## label step_major step_minor label_minor bgn end elapsed
## 17 fit.models 8 1 1 347.34 370.099 22.76
## 18 fit.models 8 2 2 370.10 NA NA```{r fit.models_2, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 371.243 NA NA## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"## Warning: Removed 3 rows containing missing values (geom_errorbar).## quartz_off_screen
## 2## Warning: Removed 3 rows containing missing values (geom_errorbar).
## id max.Accuracy.OOB max.AUCROCR.OOB
## 7 All.X##rcv#glmnet 0.5867580 0.6153233
## 6 Low.cor.X##rcv#glmnet 0.5730594 0.6077246
## 5 Interact.High.cor.Y##rcv#glmnet 0.5662100 0.5851273
## 3 Max.cor.Y.rcv.1X1###glmnet 0.5410959 0.5584215
## 4 Max.cor.Y##rcv#rpart 0.5410959 0.5448590
## 2 Random###myrandom_classfr 0.5365297 0.5042029
## 1 MFO###myMFO_classfr 0.5365297 0.5000000
## max.AUCpROC.OOB min.elapsedtime.everything max.Accuracy.fit
## 7 0.5683681 7.303 0.5689194
## 6 0.5571848 6.595 0.5631921
## 5 0.5598575 2.492 0.5578465
## 3 0.5441673 0.710 0.5595647
## 4 0.5441673 1.464 0.5595647
## 2 0.5576145 0.381 0.5360825
## 1 0.5000000 0.485 0.5360825
## opt.prob.threshold.fit opt.prob.threshold.OOB
## 7 0.45 0.40
## 6 0.45 0.50
## 5 0.50 0.50
## 3 0.50 0.50
## 4 0.50 0.50
## 2 0.55 0.55
## 1 0.50 0.50## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB + min.elapsedtime.everything -
## max.Accuracy.fit - opt.prob.threshold.OOB
## <environment: 0x7ff638d95400>## [1] "Best model id: All.X##rcv#glmnet"## glmnet
##
## 1746 samples
## 109 predictor
## 2 classes: 'D', 'R'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold, repeated 3 times)
## Summary of sample sizes: 1164, 1164, 1164, 1164, 1164, 1164, ...
## Resampling results across tuning parameters:
##
## alpha lambda Accuracy Kappa
## 0.100 5.319575e-05 0.5515464 0.09709774
## 0.100 2.469128e-04 0.5515464 0.09709774
## 0.100 1.146068e-03 0.5544101 0.10259571
## 0.100 5.319575e-03 0.5610920 0.11485313
## 0.100 2.469128e-02 0.5689194 0.12805093
## 0.325 5.319575e-05 0.5517373 0.09751032
## 0.325 2.469128e-04 0.5517373 0.09745873
## 0.325 1.146068e-03 0.5563192 0.10619634
## 0.325 5.319575e-03 0.5620466 0.11614994
## 0.325 2.469128e-02 0.5610920 0.10795427
## 0.550 5.319575e-05 0.5525010 0.09894666
## 0.550 2.469128e-04 0.5523100 0.09853826
## 0.550 1.146068e-03 0.5584192 0.11035572
## 0.550 5.319575e-03 0.5635739 0.11803988
## 0.550 2.469128e-02 0.5666285 0.11464516
## 0.775 5.319575e-05 0.5519282 0.09776565
## 0.775 2.469128e-04 0.5534555 0.10069456
## 0.775 1.146068e-03 0.5574647 0.10792503
## 0.775 5.319575e-03 0.5637648 0.11765608
## 0.775 2.469128e-02 0.5668194 0.10887915
## 1.000 5.319575e-05 0.5515464 0.09694706
## 1.000 2.469128e-04 0.5536464 0.10110872
## 1.000 1.146068e-03 0.5572738 0.10745141
## 1.000 5.319575e-03 0.5668194 0.12271698
## 1.000 2.469128e-02 0.5641466 0.09678870
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were alpha = 0.1 and lambda
## = 0.02469128.## [1] "All.X##rcv#glmnet fit prediction diagnostics:"## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"## All.X..rcv.glmnet.imp imp
## Hhold.fctrPKy 100.00000000 100.00000000
## Q98197.fctrNo 65.04828571 65.04828571
## Q101163.fctrMom 58.70889930 58.70889930
## Q123464.fctrYes 58.37654216 58.37654216
## Q100562.fctrNo 56.99103709 56.99103709
## Q108950.fctrRisk-friendly 54.82548652 54.82548652
## Q115611.fctrYes 54.36119266 54.36119266
## Q113181.fctrYes 53.87955132 53.87955132
## Q100689.fctrYes 51.33794846 51.33794846
## Q98869.fctrNo 49.75750190 49.75750190
## Q101596.fctrYes 48.66777819 48.66777819
## Q116601.fctrNo 48.16811052 48.16811052
## Q99480.fctrYes 45.37060282 45.37060282
## Q120650.fctrNo 45.12703792 45.12703792
## Q108856.fctrSocialize 44.04979703 44.04979703
## Q102674.fctrYes 42.37404254 42.37404254
## Q98059.fctrOnly-child 41.81605007 41.81605007
## Q113583.fctrTunes 40.14270897 40.14270897
## Q122771.fctrPt 38.59326056 38.59326056
## YOB.Age.fctr.L 38.50461782 38.50461782
## Q112478.fctrNo 37.76366344 37.76366344
## Q115390.fctrNo 35.85133428 35.85133428
## Q112270.fctrYes 34.69973543 34.69973543
## Q102089.fctrRent 34.32823140 34.32823140
## Q120379.fctrYes 33.89015581 33.89015581
## Edn.fctr^4 33.47414357 33.47414357
## YOB.Age.fctr.Q 32.72523076 32.72523076
## Q116441.fctrYes 32.56134900 32.56134900
## Q116441.fctrNo 31.96112168 31.96112168
## Gender.fctrM 30.99175680 30.99175680
## Q109367.fctrNo 30.76271919 30.76271919
## Q108855.fctrUmm... 30.65122953 30.65122953
## Q101162.fctrPessimist 30.46431892 30.46431892
## Q110740.fctrPC 29.99555729 29.99555729
## Q115899.fctrCs 29.83100896 29.83100896
## Q106389.fctrNo 29.79700650 29.79700650
## Q100010.fctrNo 29.53498245 29.53498245
## Q106388.fctrNo 28.71415220 28.71415220
## Q106389.fctrYes 27.24662484 27.24662484
## Q115611.fctrNo 26.63256892 26.63256892
## Income.fctr.C 26.58502973 26.58502973
## Q108342.fctrOnline 26.51987332 26.51987332
## Q104996.fctrNo 25.76255553 25.76255553
## Q120194.fctrStudy first 25.25057298 25.25057298
## Q102089.fctrOwn 25.19677217 25.19677217
## Q122120.fctrYes 25.14327685 25.14327685
## Q107491.fctrYes 25.10716089 25.10716089
## Q115195.fctrYes 25.00880052 25.00880052
## Q108617.fctrNo 24.86148060 24.86148060
## Q116797.fctrNo 24.84439074 24.84439074
## Edn.fctr.Q 24.48897959 24.48897959
## Q112512.fctrNo 24.13237615 24.13237615
## Q98197.fctrYes 23.92002846 23.92002846
## Q99716.fctrNo 23.15453176 23.15453176
## Q118232.fctrPr 23.05912330 23.05912330
## Q98578.fctrYes 22.69548452 22.69548452
## Q116197.fctrP.M. 21.74573471 21.74573471
## Q100680.fctrNo 21.32491293 21.32491293
## Q115610.fctrNo 21.26751769 21.26751769
## Q119851.fctrNo 20.74322494 20.74322494
## Q110740.fctrMac 20.43226420 20.43226420
## Q111848.fctrNo 20.14164588 20.14164588
## Q98578.fctrNo 20.02779169 20.02779169
## Income.fctr^4 19.96596182 19.96596182
## Q115777.fctrEnd 19.74269986 19.74269986
## Q114386.fctrMysterious 19.45040116 19.45040116
## Q106388.fctrYes 19.44404023 19.44404023
## Q116601.fctrYes 19.11653953 19.11653953
## Q99480.fctrNo 19.02748347 19.02748347
## Q117186.fctrCool headed 18.79970907 18.79970907
## Q102674.fctrNo 18.68634335 18.68634335
## Q115195.fctrNo 18.53860388 18.53860388
## Q120472.fctrScience 18.46387937 18.46387937
## Q120014.fctrNo 18.25595493 18.25595493
## Q116448.fctrNo 17.38150488 17.38150488
## Q109367.fctrYes 17.30956652 17.30956652
## Edn.fctr^5 17.18869620 17.18869620
## Q102289.fctrYes 17.16212334 17.16212334
## Q114961.fctrYes 16.79250041 16.79250041
## Q106272.fctrNo 16.77475600 16.77475600
## Q118117.fctrYes 16.71307330 16.71307330
## Q118892.fctrYes 16.65291627 16.65291627
## Q116953.fctrYes 16.38551337 16.38551337
## Q121700.fctrYes 16.10352224 16.10352224
## Hhold.fctrPKn 15.64519624 15.64519624
## YOB.Age.fctr^7 15.50614515 15.50614515
## Q120978.fctrYes 15.30358299 15.30358299
## Q120650.fctrYes 15.29265166 15.29265166
## Q107491.fctrNo 15.26966483 15.26966483
## Hhold.fctrMKy 14.86167522 14.86167522
## Q101162.fctrOptimist 14.79344561 14.79344561
## Q121699.fctrYes 14.69471751 14.69471751
## Q106042.fctrNo 14.31677466 14.31677466
## Q121011.fctrNo 14.27787962 14.27787962
## Edn.fctr^6 14.02376046 14.02376046
## Q123621.fctrNo 13.99411708 13.99411708
## Q101163.fctrDad 13.87998117 13.87998117
## YOB.Age.fctr.C 13.67668034 13.67668034
## Q120012.fctrNo 13.53443016 13.53443016
## Edn.fctr^7 13.33454917 13.33454917
## Q118232.fctrId 12.82468958 12.82468958
## Q101596.fctrNo 12.59209022 12.59209022
## Hhold.fctrSKn 12.58809153 12.58809153
## Q106993.fctrNo 12.46648755 12.46648755
## Income.fctr^6 12.20797580 12.20797580
## Q106272.fctrYes 12.09566958 12.09566958
## Q114386.fctrTMI 12.09039160 12.09039160
## Q116953.fctrNo 12.04415904 12.04415904
## Q116797.fctrYes 11.90204138 11.90204138
## Q115777.fctrStart 11.56229740 11.56229740
## Q120379.fctrNo 11.45508668 11.45508668
## Q116881.fctrRight 11.31786995 11.31786995
## Q117186.fctrHot headed 11.14929669 11.14929669
## Q114517.fctrNo 11.12476612 11.12476612
## Q108856.fctrSpace 10.95016791 10.95016791
## Q96024.fctrYes 10.51724456 10.51724456
## Q113181.fctrNo 10.21655090 10.21655090
## YOB.Age.fctr^8 10.19324341 10.19324341
## Q112270.fctrNo 10.16978124 10.16978124
## Q99982.fctrCheck! 9.99813228 9.99813228
## Q118233.fctrNo 9.98753273 9.98753273
## Q108342.fctrIn-person 9.87207559 9.87207559
## Q103293.fctrNo 9.13975875 9.13975875
## Q102687.fctrYes 9.10956985 9.10956985
## Q104996.fctrYes 9.06503107 9.06503107
## Q121699.fctrNo 8.90787235 8.90787235
## Q124742.fctrYes 8.43567189 8.43567189
## Q119334.fctrYes 8.31937455 8.31937455
## Q119650.fctrReceiving 8.14856004 8.14856004
## Q111848.fctrYes 8.04210993 8.04210993
## Q124122.fctrNo 7.97783846 7.97783846
## Edn.fctr.C 7.94753921 7.94753921
## Q119650.fctrGiving 7.93463365 7.93463365
## Q99581.fctrYes 7.63612487 7.63612487
## Q122770.fctrNo 7.53984667 7.53984667
## Q115602.fctrNo 7.49895999 7.49895999
## Q119851.fctrYes 7.49822551 7.49822551
## Q100689.fctrNo 7.40909895 7.40909895
## Q109244.fctrNA:.clusterid.fctr3 7.40697169 7.40697169
## YOB.Age.fctr^4 7.32963244 7.32963244
## Q113584.fctrPeople 6.97987555 6.97987555
## Gender.fctrF 6.85183036 6.85183036
## Q105840.fctrNo 6.75070028 6.75070028
## YOB.Age.fctr(35,40]:YOB.Age.dff 6.71908081 6.71908081
## Q115899.fctrMe 6.43902811 6.43902811
## Q102687.fctrNo 6.38732137 6.38732137
## Q98059.fctrYes 6.35401501 6.35401501
## Q107869.fctrYes 6.34735376 6.34735376
## Q106042.fctrYes 5.86829658 5.86829658
## Q119334.fctrNo 5.77603390 5.77603390
## Q113992.fctrYes 5.76189801 5.76189801
## Q114961.fctrNo 5.64204447 5.64204447
## Q102906.fctrYes 5.47225482 5.47225482
## Q108343.fctrYes 5.38349362 5.38349362
## Q120014.fctrYes 5.26971744 5.26971744
## Income.fctr.Q 5.01776984 5.01776984
## Edn.fctr.L 4.93985986 4.93985986
## YOB.Age.fctr(20,25]:YOB.Age.dff 4.78865841 4.78865841
## Q96024.fctrNo 4.51911502 4.51911502
## Q114748.fctrNo 4.44030358 4.44030358
## Hhold.fctrMKn 3.96941185 3.96941185
## YOB.Age.fctr^5 3.32559612 3.32559612
## Q98078.fctrNo 3.22890502 3.22890502
## Q116448.fctrYes 3.16653709 3.16653709
## Q118892.fctrNo 3.16183870 3.16183870
## Q111580.fctrSupportive 3.08478749 3.08478749
## Q114152.fctrYes 2.97986267 2.97986267
## Q118237.fctrNo 2.91954724 2.91954724
## Q106997.fctrGr 2.91187343 2.91187343
## Q122771.fctrPc 2.84528130 2.84528130
## Q117193.fctrStandard hours 2.69447397 2.69447397
## Q108950.fctrCautious 2.44007671 2.44007671
## Q124122.fctrYes 2.30458741 2.30458741
## Q109244.fctrNA:.clusterid.fctr2 2.09361358 2.09361358
## Q115610.fctrYes 1.87689570 1.87689570
## Q112512.fctrYes 1.54061708 1.54061708
## YOB.Age.fctr(15,20]:YOB.Age.dff 1.47081274 1.47081274
## .rnorm 1.31590903 1.31590903
## Q121700.fctrNo 1.26704991 1.26704991
## Q117193.fctrOdd hours 0.97986083 0.97986083
## Q98869.fctrYes 0.83171509 0.83171509
## Q116197.fctrA.M. 0.47480200 0.47480200
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.26203741 0.26203741
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.22402287 0.22402287
## Q113992.fctrNo 0.12317152 0.12317152
## Q102289.fctrNo 0.01831279 0.01831279
## Hhold.fctrSKy 0.00000000 0.00000000
## Income.fctr.L 0.00000000 0.00000000
## Income.fctr^5 0.00000000 0.00000000
## Q100010.fctrYes 0.00000000 0.00000000
## Q100562.fctrYes 0.00000000 0.00000000
## Q100680.fctrYes 0.00000000 0.00000000
## Q102906.fctrNo 0.00000000 0.00000000
## Q103293.fctrYes 0.00000000 0.00000000
## Q105655.fctrNo 0.00000000 0.00000000
## Q105655.fctrYes 0.00000000 0.00000000
## Q105840.fctrYes 0.00000000 0.00000000
## Q106993.fctrYes 0.00000000 0.00000000
## Q106997.fctrYy 0.00000000 0.00000000
## Q107869.fctrNo 0.00000000 0.00000000
## Q108343.fctrNo 0.00000000 0.00000000
## Q108617.fctrYes 0.00000000 0.00000000
## Q108754.fctrNo 0.00000000 0.00000000
## Q108754.fctrYes 0.00000000 0.00000000
## Q108855.fctrYes! 0.00000000 0.00000000
## Q111220.fctrNo 0.00000000 0.00000000
## Q111220.fctrYes 0.00000000 0.00000000
## Q111580.fctrDemanding 0.00000000 0.00000000
## Q112478.fctrYes 0.00000000 0.00000000
## Q113583.fctrTalk 0.00000000 0.00000000
## Q113584.fctrTechnology 0.00000000 0.00000000
## Q114152.fctrNo 0.00000000 0.00000000
## Q114517.fctrYes 0.00000000 0.00000000
## Q114748.fctrYes 0.00000000 0.00000000
## Q115390.fctrYes 0.00000000 0.00000000
## Q115602.fctrYes 0.00000000 0.00000000
## Q116881.fctrHappy 0.00000000 0.00000000
## Q118117.fctrNo 0.00000000 0.00000000
## Q118233.fctrYes 0.00000000 0.00000000
## Q118237.fctrYes 0.00000000 0.00000000
## Q120012.fctrYes 0.00000000 0.00000000
## Q120194.fctrTry first 0.00000000 0.00000000
## Q120472.fctrArt 0.00000000 0.00000000
## Q120978.fctrNo 0.00000000 0.00000000
## Q121011.fctrYes 0.00000000 0.00000000
## Q122120.fctrNo 0.00000000 0.00000000
## Q122769.fctrNo 0.00000000 0.00000000
## Q122769.fctrYes 0.00000000 0.00000000
## Q122770.fctrYes 0.00000000 0.00000000
## Q123464.fctrNo 0.00000000 0.00000000
## Q123621.fctrYes 0.00000000 0.00000000
## Q124742.fctrNo 0.00000000 0.00000000
## Q98078.fctrYes 0.00000000 0.00000000
## Q99581.fctrNo 0.00000000 0.00000000
## Q99716.fctrYes 0.00000000 0.00000000
## Q99982.fctrNope 0.00000000 0.00000000
## YOB.Age.fctr^6 0.00000000 0.00000000
## Q109244.fctrNA:.clusterid.fctr1 0.00000000 0.00000000
## Q109244.fctrNo:.clusterid.fctr1 0.00000000 0.00000000
## Q109244.fctrYes:.clusterid.fctr1 0.00000000 0.00000000
## Q109244.fctrNo:.clusterid.fctr2 0.00000000 0.00000000
## Q109244.fctrYes:.clusterid.fctr2 0.00000000 0.00000000
## Q109244.fctrNo:.clusterid.fctr3 0.00000000 0.00000000
## Q109244.fctrYes:.clusterid.fctr3 0.00000000 0.00000000
## Q109244.fctrNA:.clusterid.fctr4 0.00000000 0.00000000
## Q109244.fctrNo:.clusterid.fctr4 0.00000000 0.00000000
## Q109244.fctrYes:.clusterid.fctr4 0.00000000 0.00000000
## YOB.Age.fctrNA:YOB.Age.dff 0.00000000 0.00000000
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.00000000 0.00000000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.00000000 0.00000000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.00000000 0.00000000## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 108
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 1792 R 0.1026305
## 2 6339 R 0.1723108
## 3 2250 R 0.1931380
## 4 5299 R 0.2032839
## 5 6098 R 0.2165230
## 6 2774 R 0.2180307
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 D TRUE
## 2 D TRUE
## 3 D TRUE
## 4 D TRUE
## 5 D TRUE
## 6 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.8973695 FALSE
## 2 0.8276892 FALSE
## 3 0.8068620 FALSE
## 4 0.7967161 FALSE
## 5 0.7834770 FALSE
## 6 0.7819693 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1 FALSE -0.2973695
## 2 FALSE -0.2276892
## 3 FALSE -0.2068620
## 4 FALSE -0.1967161
## 5 FALSE -0.1834770
## 6 FALSE -0.1819693
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 5 6098 R 0.2165230
## 48 5421 R 0.3716921
## 124 1927 D 0.5574197
## 137 6803 D 0.5849008
## 141 6604 D 0.5916924
## 168 5764 D 0.7034418
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 5 D TRUE
## 48 D TRUE
## 124 R TRUE
## 137 R TRUE
## 141 R TRUE
## 168 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 5 0.7834770
## 48 0.6283079
## 124 0.5574197
## 137 0.5849008
## 141 0.5916924
## 168 0.7034418
## Party.fctr.All.X..rcv.glmnet.is.acc
## 5 FALSE
## 48 FALSE
## 124 FALSE
## 137 FALSE
## 141 FALSE
## 168 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 5 FALSE
## 48 FALSE
## 124 FALSE
## 137 FALSE
## 141 FALSE
## 168 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 5 -0.18347703
## 48 -0.02830791
## 124 0.15741970
## 137 0.18490082
## 141 0.19169243
## 168 0.30344177
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 176 5435 D 0.8050629
## 177 486 D 0.8231301
## 178 2888 D 0.8355269
## 179 6053 D 0.8572648
## 180 4350 D 0.9053727
## 181 3431 D 0.9266629
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 176 R TRUE
## 177 R TRUE
## 178 R TRUE
## 179 R TRUE
## 180 R TRUE
## 181 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 176 0.8050629
## 177 0.8231301
## 178 0.8355269
## 179 0.8572648
## 180 0.9053727
## 181 0.9266629
## Party.fctr.All.X..rcv.glmnet.is.acc
## 176 FALSE
## 177 FALSE
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 176 FALSE
## 177 FALSE
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 176 0.4050629
## 177 0.4231301
## 178 0.4355269
## 179 0.4572648
## 180 0.5053727
## 181 0.5266629
## Q109244.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## NA NA 438 1746 547 1 1
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## NA 1 737.9715 0.4226641 1746 202.5673
## err.abs.OOB.mean
## NA 0.4624824
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 438.0000000 1746.0000000 547.0000000 1.0000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.0000000 1.0000000 737.9714939 0.4226641
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 1746.0000000 202.5673105 0.4624824## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 377.571 NA NA## label step_major step_minor label_minor bgn end elapsed
## 18 fit.models 8 2 2 370.100 377.582 7.482
## 19 fit.models 8 3 3 377.582 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 19 fit.models 8 3 3 377.582 380.781
## 20 fit.data.training 9 0 0 380.781 NA
## elapsed
## 19 3.199
## 20 NAStep 9.0: fit data training
```{r fit.data.training_0, cache=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Final.All.X###glmnet"
## [1] " indepVar: Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q100562.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q102674.fctr,Q114386.fctr,Q98078.fctr,Q102089.fctr,Edn.fctr,Q100689.fctr,Q113583.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.693000 secs"
## Fitting alpha = 0.1, lambda = 0.0247 on full training set
## [1] "myfit_mdl: train complete: 2.546000 secs"
## alpha lambda
## 1 0.1 0.02469128
## Length Class Mode
## a0 75 -none- numeric
## beta 18825 dgCMatrix S4
## df 75 -none- numeric
## dim 2 -none- numeric
## lambda 75 -none- numeric
## dev.ratio 75 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 251 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) .rnorm
## -0.155735522 0.048497881
## Edn.fctr.L Edn.fctr.Q
## -0.042351538 0.193191919
## Edn.fctr.C Edn.fctr^4
## 0.033138813 0.241901212
## Edn.fctr^5 Edn.fctr^6
## 0.043463735 -0.006338041
## Edn.fctr^7 Gender.fctrF
## -0.197671892 -0.059925060
## Gender.fctrM Hhold.fctrMKy
## 0.228861507 0.061066645
## Hhold.fctrPKn Hhold.fctrPKy
## -0.285177488 0.474419006
## Hhold.fctrSKn Hhold.fctrSKy
## -0.093339977 0.084469892
## Income.fctr.L Income.fctr.Q
## 0.004233102 0.020221507
## Income.fctr.C Income.fctr^4
## 0.246940056 0.007269751
## Income.fctr^6 Q100010.fctrNo
## 0.047397894 -0.203222287
## Q100562.fctrNo Q100680.fctrNo
## 0.417843950 -0.089933103
## Q100689.fctrNo Q100689.fctrYes
## -0.101644312 -0.318289690
## Q101162.fctrOptimist Q101162.fctrPessimist
## -0.250338072 0.052653209
## Q101163.fctrDad Q101163.fctrMom
## 0.118126362 -0.507022157
## Q101596.fctrNo Q101596.fctrYes
## 0.121379410 0.435164069
## Q102089.fctrOwn Q102089.fctrRent
## -0.111784825 -0.109419905
## Q102289.fctrYes Q102674.fctrNo
## -0.028835661 0.135270022
## Q102674.fctrYes Q102687.fctrNo
## -0.363293688 0.003480660
## Q102687.fctrYes Q103293.fctrNo
## -0.046717749 0.034812752
## Q104996.fctrNo Q105655.fctrYes
## 0.171833641 0.016119524
## Q105840.fctrNo Q105840.fctrYes
## 0.011438126 -0.043312984
## Q106042.fctrNo Q106042.fctrYes
## 0.061266352 0.048655135
## Q106272.fctrYes Q106388.fctrNo
## 0.084804411 0.121996345
## Q106388.fctrYes Q106389.fctrNo
## -0.015247735 0.259718791
## Q106389.fctrYes Q106993.fctrNo
## -0.218895766 0.079702105
## Q106993.fctrYes Q107491.fctrNo
## 0.007173634 0.173782956
## Q107491.fctrYes Q107869.fctrYes
## -0.117444407 0.056894720
## Q108342.fctrIn-person Q108342.fctrOnline
## -0.078369968 -0.389946988
## Q108343.fctrNo Q108617.fctrNo
## -0.008904055 0.176498467
## Q108855.fctrUmm... Q108856.fctrSocialize
## 0.261953383 0.425563533
## Q108856.fctrSpace Q108950.fctrCautious
## -0.062734745 -0.041349491
## Q108950.fctrRisk-friendly Q109367.fctrYes
## -0.404759064 -0.169860866
## Q110740.fctrMac Q110740.fctrPC
## 0.101820545 0.178432707
## Q111580.fctrDemanding Q111580.fctrSupportive
## 0.028590482 -0.016548159
## Q111848.fctrNo Q111848.fctrYes
## -0.018612662 -0.091270380
## Q112270.fctrNo Q112270.fctrYes
## -0.154941982 -0.261261506
## Q112478.fctrNo Q112512.fctrYes
## 0.227655081 0.001232412
## Q113181.fctrNo Q113181.fctrYes
## -0.105345625 0.441107670
## Q113583.fctrTalk Q113583.fctrTunes
## 0.032912378 -0.319259662
## Q113584.fctrPeople Q113992.fctrYes
## -0.029603101 -0.153342195
## Q114152.fctrNo Q114386.fctrMysterious
## 0.072193969 0.068137889
## Q114386.fctrTMI Q114517.fctrNo
## -0.116335033 -0.087448270
## Q114517.fctrYes Q114961.fctrNo
## -0.011724395 -0.082962955
## Q114961.fctrYes Q115195.fctrNo
## 0.074368142 0.203504171
## Q115195.fctrYes Q115390.fctrNo
## -0.108531722 0.292109514
## Q115390.fctrYes Q115602.fctrNo
## -0.054106733 -0.020592405
## Q115611.fctrNo Q115611.fctrYes
## -0.237233630 0.411027888
## Q115777.fctrEnd Q115777.fctrStart
## -0.149096992 0.014511518
## Q115899.fctrCs Q115899.fctrMe
## -0.123643551 0.068877613
## Q116197.fctrA.M. Q116197.fctrP.M.
## 0.047572789 0.173849246
## Q116441.fctrNo Q116441.fctrYes
## -0.167402287 0.247490339
## Q116448.fctrNo Q116601.fctrNo
## 0.004771013 0.360764668
## Q116601.fctrYes Q116797.fctrNo
## -0.193406319 0.100975739
## Q116797.fctrYes Q116881.fctrHappy
## 0.026701929 -0.056827720
## Q116881.fctrRight Q116953.fctrNo
## 0.080966634 -0.048335737
## Q116953.fctrYes Q117186.fctrCool headed
## -0.018545109 -0.132569790
## Q117186.fctrHot headed Q117193.fctrOdd hours
## 0.073624353 -0.022905890
## Q118117.fctrYes Q118232.fctrId
## 0.050181001 -0.162544078
## Q118232.fctrPr Q118233.fctrNo
## -0.187927698 0.111321953
## Q118233.fctrYes Q118237.fctrNo
## 0.020997792 0.055939690
## Q118237.fctrYes Q118892.fctrNo
## 0.151362010 0.064459710
## Q118892.fctrYes Q119334.fctrNo
## -0.118309250 -0.035568239
## Q119334.fctrYes Q119650.fctrReceiving
## 0.055323684 0.029184660
## Q119851.fctrNo Q119851.fctrYes
## 0.223356813 -0.093763963
## Q120012.fctrNo Q120012.fctrYes
## 0.079509216 -0.037475190
## Q120014.fctrNo Q120194.fctrStudy first
## -0.062424533 -0.174101517
## Q120194.fctrTry first Q120379.fctrNo
## -0.055955869 0.112649301
## Q120379.fctrYes Q120472.fctrScience
## -0.162600689 0.133664785
## Q120650.fctrNo Q120650.fctrYes
## -0.320628227 0.174754865
## Q120978.fctrYes Q121011.fctrNo
## -0.032742146 0.099002524
## Q121699.fctrNo Q121699.fctrYes
## -0.005350878 -0.158072511
## Q121700.fctrYes Q122120.fctrNo
## -0.140472937 0.021819384
## Q122120.fctrYes Q122769.fctrNo
## 0.190361999 0.047735034
## Q122770.fctrNo Q122770.fctrYes
## -0.121762115 0.044963124
## Q122771.fctrPc Q122771.fctrPt
## -0.077074804 0.210204411
## Q123464.fctrYes Q123621.fctrNo
## -0.285560018 0.108394236
## Q124122.fctrNo Q124122.fctrYes
## 0.118732992 0.013414727
## Q124742.fctrNo Q124742.fctrYes
## 0.018102582 0.023871442
## Q96024.fctrNo Q98059.fctrOnly-child
## -0.023149480 0.473171414
## Q98059.fctrYes Q98078.fctrNo
## -0.049531068 -0.054075183
## Q98197.fctrNo Q98197.fctrYes
## -0.499136744 0.159643726
## Q98578.fctrNo Q98578.fctrYes
## 0.165560431 0.095720502
## Q98869.fctrNo Q99480.fctrNo
## -0.402958797 -0.117039476
## Q99480.fctrYes Q99581.fctrNo
## 0.321839509 0.059302405
## Q99581.fctrYes Q99716.fctrNo
## 0.096054707 -0.173201515
## YOB.Age.fctr.L YOB.Age.fctr.Q
## -0.293339238 -0.269655747
## YOB.Age.fctr.C YOB.Age.fctr^4
## -0.044817098 -0.082642447
## YOB.Age.fctr^8 Q109244.fctrNA:.clusterid.fctr1
## 0.086869050 0.004750456
## Q109244.fctrNA:.clusterid.fctr2 Q109244.fctrNA:.clusterid.fctr3
## -0.112960582 0.064150685
## YOB.Age.fctr(15,20]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.010906574 -0.093751767
## [1] "max lambda < lambdaOpt:"
## (Intercept) .rnorm
## -1.621359e-01 4.937408e-02
## Edn.fctr.L Edn.fctr.Q
## -4.113826e-02 2.038823e-01
## Edn.fctr.C Edn.fctr^4
## 4.036092e-02 2.482406e-01
## Edn.fctr^5 Edn.fctr^6
## 5.311455e-02 -8.704239e-03
## Edn.fctr^7 Gender.fctrF
## -2.036835e-01 -5.529399e-02
## Gender.fctrM Hhold.fctrMKy
## 2.356193e-01 6.362269e-02
## Hhold.fctrPKn Hhold.fctrPKy
## -2.897303e-01 4.912855e-01
## Hhold.fctrSKn Hhold.fctrSKy
## -9.517399e-02 9.609208e-02
## Income.fctr.L Income.fctr.Q
## 6.582764e-03 2.280676e-02
## Income.fctr.C Income.fctr^4
## 2.537842e-01 1.205964e-02
## Income.fctr^6 Q100010.fctrNo
## 5.114986e-02 -2.139541e-01
## Q100562.fctrNo Q100680.fctrNo
## 4.348235e-01 -9.904885e-02
## Q100689.fctrNo Q100689.fctrYes
## -1.141361e-01 -3.389443e-01
## Q101162.fctrOptimist Q101162.fctrPessimist
## -2.598687e-01 5.567984e-02
## Q101163.fctrDad Q101163.fctrMom
## 1.222992e-01 -5.212616e-01
## Q101596.fctrNo Q101596.fctrYes
## 1.384897e-01 4.593971e-01
## Q102089.fctrOwn Q102089.fctrRent
## -1.266792e-01 -1.207326e-01
## Q102289.fctrYes Q102674.fctrNo
## -3.425080e-02 1.401204e-01
## Q102674.fctrYes Q102687.fctrNo
## -3.724177e-01 9.197990e-03
## Q102687.fctrYes Q102906.fctrNo
## -4.788108e-02 2.928707e-03
## Q103293.fctrNo Q104996.fctrNo
## 3.907181e-02 1.775747e-01
## Q105655.fctrYes Q105840.fctrNo
## 2.289021e-02 8.012511e-03
## Q105840.fctrYes Q106042.fctrNo
## -4.987565e-02 6.853146e-02
## Q106042.fctrYes Q106272.fctrYes
## 5.708849e-02 8.917524e-02
## Q106388.fctrNo Q106388.fctrYes
## 1.264224e-01 -2.151703e-02
## Q106389.fctrNo Q106389.fctrYes
## 2.629391e-01 -2.333123e-01
## Q106993.fctrNo Q106993.fctrYes
## 8.885987e-02 1.192608e-02
## Q107491.fctrNo Q107491.fctrYes
## 1.754278e-01 -1.294912e-01
## Q107869.fctrYes Q108342.fctrIn-person
## 6.747350e-02 -8.892355e-02
## Q108342.fctrOnline Q108343.fctrNo
## -4.099420e-01 -7.074764e-03
## Q108343.fctrYes Q108617.fctrNo
## 8.223200e-03 1.893368e-01
## Q108855.fctrUmm... Q108856.fctrSocialize
## 2.757209e-01 4.454879e-01
## Q108856.fctrSpace Q108950.fctrCautious
## -6.649432e-02 -5.350408e-02
## Q108950.fctrRisk-friendly Q109367.fctrNo
## -4.214514e-01 -7.038724e-03
## Q109367.fctrYes Q110740.fctrMac
## -1.759965e-01 1.101285e-01
## Q110740.fctrPC Q111580.fctrDemanding
## 1.876568e-01 3.610544e-02
## Q111580.fctrSupportive Q111848.fctrNo
## -1.557874e-02 -2.552821e-02
## Q111848.fctrYes Q112270.fctrNo
## -9.803264e-02 -1.661432e-01
## Q112270.fctrYes Q112478.fctrNo
## -2.765758e-01 2.378109e-01
## Q112512.fctrYes Q113181.fctrNo
## 9.129838e-03 -1.074855e-01
## Q113181.fctrYes Q113583.fctrTalk
## 4.501721e-01 3.563799e-02
## Q113583.fctrTunes Q113584.fctrPeople
## -3.295296e-01 -3.745099e-02
## Q113992.fctrYes Q114152.fctrNo
## -1.601692e-01 8.020392e-02
## Q114152.fctrYes Q114386.fctrMysterious
## -2.332660e-03 7.347392e-02
## Q114386.fctrTMI Q114517.fctrNo
## -1.167745e-01 -9.715113e-02
## Q114517.fctrYes Q114961.fctrNo
## -2.214456e-02 -8.526873e-02
## Q114961.fctrYes Q115195.fctrNo
## 8.161552e-02 2.054169e-01
## Q115195.fctrYes Q115390.fctrNo
## -1.162603e-01 3.000349e-01
## Q115390.fctrYes Q115602.fctrNo
## -5.505851e-02 -2.812334e-02
## Q115611.fctrNo Q115611.fctrYes
## -2.395182e-01 4.201866e-01
## Q115777.fctrEnd Q115777.fctrStart
## -1.581595e-01 1.412591e-02
## Q115899.fctrCs Q115899.fctrMe
## -1.303256e-01 7.413105e-02
## Q116197.fctrA.M. Q116197.fctrP.M.
## 6.078095e-02 1.859349e-01
## Q116441.fctrNo Q116441.fctrYes
## -1.741618e-01 2.498662e-01
## Q116448.fctrNo Q116601.fctrNo
## 8.958500e-03 3.607235e-01
## Q116601.fctrYes Q116797.fctrNo
## -2.047098e-01 1.158964e-01
## Q116797.fctrYes Q116881.fctrHappy
## 3.980174e-02 -5.831995e-02
## Q116881.fctrRight Q116953.fctrNo
## 8.390039e-02 -6.333457e-02
## Q116953.fctrYes Q117186.fctrCool headed
## -2.900009e-02 -1.385066e-01
## Q117186.fctrHot headed Q117193.fctrOdd hours
## 7.475897e-02 -2.977243e-02
## Q118117.fctrYes Q118232.fctrId
## 5.163478e-02 -1.811687e-01
## Q118232.fctrPr Q118233.fctrNo
## -2.060572e-01 1.234449e-01
## Q118233.fctrYes Q118237.fctrNo
## 3.224856e-02 6.363322e-02
## Q118237.fctrYes Q118892.fctrNo
## 1.608285e-01 6.384954e-02
## Q118892.fctrYes Q119334.fctrNo
## -1.225044e-01 -3.780577e-02
## Q119334.fctrYes Q119650.fctrReceiving
## 6.013642e-02 3.407147e-02
## Q119851.fctrNo Q119851.fctrYes
## 2.301931e-01 -9.264659e-02
## Q120012.fctrNo Q120012.fctrYes
## 8.428296e-02 -3.705426e-02
## Q120014.fctrNo Q120194.fctrStudy first
## -6.439023e-02 -1.898417e-01
## Q120194.fctrTry first Q120379.fctrNo
## -6.889291e-02 1.191147e-01
## Q120379.fctrYes Q120472.fctrScience
## -1.635323e-01 1.392245e-01
## Q120650.fctrNo Q120650.fctrYes
## -3.250471e-01 1.841044e-01
## Q120978.fctrYes Q121011.fctrNo
## -3.529960e-02 1.037108e-01
## Q121699.fctrNo Q121699.fctrYes
## -1.752524e-02 -1.681560e-01
## Q121700.fctrYes Q122120.fctrNo
## -1.464318e-01 3.043047e-02
## Q122120.fctrYes Q122769.fctrNo
## 2.043593e-01 5.248929e-02
## Q122770.fctrNo Q122770.fctrYes
## -1.267999e-01 4.473062e-02
## Q122771.fctrPc Q122771.fctrPt
## -8.575892e-02 2.125432e-01
## Q123464.fctrYes Q123621.fctrNo
## -2.980636e-01 1.148735e-01
## Q124122.fctrNo Q124122.fctrYes
## 1.247488e-01 1.958395e-02
## Q124742.fctrNo Q124742.fctrYes
## 1.948799e-02 2.790232e-02
## Q96024.fctrNo Q98059.fctrOnly-child
## -2.877279e-02 4.945826e-01
## Q98059.fctrYes Q98078.fctrNo
## -5.029310e-02 -6.253256e-02
## Q98197.fctrNo Q98197.fctrYes
## -5.132830e-01 1.578232e-01
## Q98578.fctrNo Q98578.fctrYes
## 1.783604e-01 1.112756e-01
## Q98869.fctrNo Q99480.fctrNo
## -4.132455e-01 -1.206124e-01
## Q99480.fctrYes Q99581.fctrNo
## 3.303394e-01 7.059475e-02
## Q99581.fctrYes Q99716.fctrNo
## 1.122950e-01 -1.848122e-01
## YOB.Age.fctr.L YOB.Age.fctr.Q
## -3.118703e-01 -2.859581e-01
## YOB.Age.fctr.C YOB.Age.fctr^4
## -5.181447e-02 -9.358444e-02
## YOB.Age.fctr^8 Q109244.fctrNA:.clusterid.fctr1
## 8.623473e-02 9.992372e-03
## Q109244.fctrNA:.clusterid.fctr2 Q109244.fctrNA:.clusterid.fctr3
## -1.220764e-01 7.846628e-02
## Q109244.fctrNA:.clusterid.fctr4 YOB.Age.fctr(15,20]:YOB.Age.dff
## -4.937098e-04 -1.398681e-02
## YOB.Age.fctr(35,40]:YOB.Age.dff YOB.Age.fctr(40,50]:YOB.Age.dff
## -9.675024e-02 -3.583804e-05
## [1] "myfit_mdl: train diagnostics complete: 2.621000 secs"
## Prediction
## Reference D R
## D 871 300
## R 417 596
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.717033e-01 3.347686e-01 6.515595e-01 6.913854e-01 5.361722e-01
## AccuracyPValue McnemarPValue
## 4.506595e-38 1.476942e-05
## [1] "myfit_mdl: predict complete: 8.293000 secs"
## id
## 1 Final.All.X###glmnet
## feats
## 1 Gender.fctr,Q113181.fctr,Q120472.fctr,Q115611.fctr,Q120650.fctr,Q118237.fctr,.rnorm,Q122120.fctr,Q110740.fctr,Q122770.fctr,Q118117.fctr,Income.fctr,Q116441.fctr,Q118233.fctr,Q106272.fctr,Q119650.fctr,Q124742.fctr,Q122771.fctr,Q99480.fctr,Q116197.fctr,Q116881.fctr,Q101596.fctr,Q122769.fctr,Q108855.fctr,Q120014.fctr,Q119334.fctr,Q106993.fctr,Q107869.fctr,Q121011.fctr,Q117186.fctr,Q106997.fctr,Q108617.fctr,Q98197.fctr,Q106042.fctr,Q115777.fctr,Q123621.fctr,Q106388.fctr,Q114152.fctr,Q124122.fctr,Q120194.fctr,Q116797.fctr,Q105655.fctr,Q115899.fctr,Q116448.fctr,Q117193.fctr,Q108754.fctr,Q108856.fctr,YOB.Age.fctr,Q123464.fctr,Q99581.fctr,Q114961.fctr,Q104996.fctr,Q108343.fctr,Q120012.fctr,Q120978.fctr,Q98578.fctr,Q103293.fctr,Q106389.fctr,Q98869.fctr,Q112512.fctr,Q116953.fctr,Q100010.fctr,Q111220.fctr,Q102906.fctr,Q121700.fctr,Q112478.fctr,Q115610.fctr,Q119851.fctr,Q114517.fctr,Q118892.fctr,Q115602.fctr,Q120379.fctr,Q107491.fctr,Q114748.fctr,Q99982.fctr,Q113992.fctr,Q115390.fctr,Q118232.fctr,Q96024.fctr,Q115195.fctr,Q121699.fctr,Q100680.fctr,Q111580.fctr,Q102289.fctr,Q102687.fctr,Q105840.fctr,Q101162.fctr,Q108950.fctr,Q116601.fctr,Q108342.fctr,Q100562.fctr,Q113584.fctr,Q109367.fctr,Q99716.fctr,Hhold.fctr,Q112270.fctr,Q98059.fctr,Q111848.fctr,Q102674.fctr,Q114386.fctr,Q98078.fctr,Q102089.fctr,Edn.fctr,Q100689.fctr,Q113583.fctr,Q101163.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 1.761 1.12
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6660801 0.7438087 0.5883514 0.7351636
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.6244107 0.6717033
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6515595 0.6913854 0.3347686
## [1] "myfit_mdl: exit: 8.316000 secs"## label step_major step_minor label_minor bgn end
## 20 fit.data.training 9 0 0 380.781 389.605
## 21 fit.data.training 9 1 1 389.605 NA
## elapsed
## 20 8.824
## 21 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFinId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFinId)$feats, ","))
if (glb_is_classification)
# mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
# mdlEnsembleComps <- gsub(paste0("^",
# gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
# "", mdlEnsembleComps)
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$prob %in% mdlEnsembleComps)] else
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlEnsembleComps)]
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
# glb_fin_mdl uses the same coefficients as glb_sel_mdl,
# so copy the "Final" columns into "non-Final" columns
glbObsTrn[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
glbObsNew[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.4glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X..rcv.glmnet.imp
## Q101163.fctrMom 58.70889930
## Q98197.fctrNo 65.04828571
## Hhold.fctrPKy 100.00000000
## Q98059.fctrOnly-child 41.81605007
## Q113181.fctrYes 53.87955132
## Q101596.fctrYes 48.66777819
## Q108856.fctrSocialize 44.04979703
## Q100562.fctrNo 56.99103709
## Q115611.fctrYes 54.36119266
## Q108950.fctrRisk-friendly 54.82548652
## Q98869.fctrNo 49.75750190
## Q108342.fctrOnline 26.51987332
## Q102674.fctrYes 42.37404254
## Q116601.fctrNo 48.16811052
## Q99480.fctrYes 45.37060282
## Q100689.fctrYes 51.33794846
## Q113583.fctrTunes 40.14270897
## Q120650.fctrNo 45.12703792
## YOB.Age.fctr.L 38.50461782
## Q115390.fctrNo 35.85133428
## Q123464.fctrYes 58.37654216
## Hhold.fctrPKn 15.64519624
## YOB.Age.fctr.Q 32.72523076
## Q108855.fctrUmm... 30.65122953
## Q112270.fctrYes 34.69973543
## Q106389.fctrNo 29.79700650
## Q101162.fctrOptimist 14.79344561
## Income.fctr.C 26.58502973
## Q116441.fctrYes 32.56134900
## Edn.fctr^4 33.47414357
## Q115611.fctrNo 26.63256892
## Gender.fctrM 30.99175680
## Q112478.fctrNo 37.76366344
## Q119851.fctrNo 20.74322494
## Q106389.fctrYes 27.24662484
## Q122771.fctrPt 38.59326056
## Q100010.fctrNo 29.53498245
## Q115195.fctrNo 18.53860388
## Edn.fctr^7 13.33454917
## Q116601.fctrYes 19.11653953
## Edn.fctr.Q 24.48897959
## Q122120.fctrYes 25.14327685
## Q118232.fctrPr 23.05912330
## Q110740.fctrPC 29.99555729
## Q108617.fctrNo 24.86148060
## Q120194.fctrStudy first 25.25057298
## Q120650.fctrYes 15.29265166
## Q116197.fctrP.M. 21.74573471
## Q99716.fctrNo 23.15453176
## Q107491.fctrNo 15.26966483
## Q104996.fctrNo 25.76255553
## Q109367.fctrYes 17.30956652
## Q116441.fctrNo 31.96112168
## Q98578.fctrNo 20.02779169
## Q118232.fctrId 12.82468958
## Q120379.fctrYes 33.89015581
## Q121699.fctrYes 14.69471751
## Q98197.fctrYes 23.92002846
## Q112270.fctrNo 10.16978124
## Q113992.fctrYes 5.76189801
## Q118237.fctrYes 0.00000000
## Q115777.fctrEnd 19.74269986
## Q121700.fctrYes 16.10352224
## Q102674.fctrNo 18.68634335
## Q120472.fctrScience 18.46387937
## Q117186.fctrCool headed 18.79970907
## Q101596.fctrNo 12.59209022
## Q115899.fctrCs 29.83100896
## Q106388.fctrNo 28.71415220
## Q122770.fctrNo 7.53984667
## Q107491.fctrYes 25.10716089
## Q124122.fctrNo 7.97783846
## Q118892.fctrYes 16.65291627
## Q101163.fctrDad 13.87998117
## Q99480.fctrNo 19.02748347
## Q114386.fctrTMI 12.09039160
## Q102089.fctrOwn 25.19677217
## Q109244.fctrNA:.clusterid.fctr2 2.09361358
## Q118233.fctrNo 9.98753273
## Q120379.fctrNo 11.45508668
## Q102089.fctrRent 34.32823140
## Q115195.fctrYes 25.00880052
## Q123621.fctrNo 13.99411708
## Q113181.fctrNo 10.21655090
## Q100689.fctrNo 7.40909895
## Q116797.fctrNo 24.84439074
## Q110740.fctrMac 20.43226420
## Q121011.fctrNo 14.27787962
## Q99581.fctrYes 7.63612487
## Q98578.fctrYes 22.69548452
## YOB.Age.fctr(35,40]:YOB.Age.dff 6.71908081
## Hhold.fctrSKn 12.58809153
## Q119851.fctrYes 7.49822551
## Q111848.fctrYes 8.04210993
## Q100680.fctrNo 21.32491293
## Q114517.fctrNo 11.12476612
## Hhold.fctrSKy 0.00000000
## YOB.Age.fctr^8 10.19324341
## Q106272.fctrYes 12.09566958
## YOB.Age.fctr^4 7.32963244
## Q114961.fctrNo 5.64204447
## Q106993.fctrNo 12.46648755
## Q116881.fctrRight 11.31786995
## Q108342.fctrIn-person 9.87207559
## Q120012.fctrNo 13.53443016
## Q122771.fctrPc 2.84528130
## Q114961.fctrYes 16.79250041
## Q114152.fctrNo 0.00000000
## Q117186.fctrHot headed 11.14929669
## Q115899.fctrMe 6.43902811
## Q114386.fctrMysterious 19.45040116
## Q109244.fctrNA:.clusterid.fctr3 7.40697169
## Q118892.fctrNo 3.16183870
## Q108856.fctrSpace 10.95016791
## Q106042.fctrNo 14.31677466
## Q120014.fctrNo 18.25595493
## Q99581.fctrNo 0.00000000
## Hhold.fctrMKy 14.86167522
## Q107869.fctrYes 6.34735376
## Q120194.fctrTry first 0.00000000
## Gender.fctrF 6.85183036
## Q118237.fctrNo 2.91954724
## Q116881.fctrHappy 0.00000000
## Q119334.fctrYes 8.31937455
## Q98078.fctrNo 3.22890502
## Q115390.fctrYes 0.00000000
## Q101162.fctrPessimist 30.46431892
## Q116953.fctrNo 12.04415904
## Q116197.fctrA.M. 0.47480200
## Q106042.fctrYes 5.86829658
## Q118117.fctrYes 16.71307330
## Q98059.fctrYes 6.35401501
## Q122769.fctrNo 0.00000000
## .rnorm 1.31590903
## Income.fctr^6 12.20797580
## Q102687.fctrYes 9.10956985
## YOB.Age.fctr.C 13.67668034
## Edn.fctr^5 17.18869620
## Q122770.fctrYes 0.00000000
## Q105840.fctrYes 0.00000000
## Q108950.fctrCautious 2.44007671
## Edn.fctr.L 4.93985986
## Q120012.fctrYes 0.00000000
## Q119334.fctrNo 5.77603390
## Q103293.fctrNo 9.13975875
## Edn.fctr.C 7.94753921
## Q113583.fctrTalk 0.00000000
## Q120978.fctrYes 15.30358299
## Q113584.fctrPeople 6.97987555
## Q111580.fctrDemanding 0.00000000
## Q119650.fctrReceiving 8.14856004
## Q102289.fctrYes 17.16212334
## Q116797.fctrYes 11.90204138
## Q124742.fctrYes 8.43567189
## Q117193.fctrOdd hours 0.97986083
## Q96024.fctrNo 4.51911502
## Q122120.fctrNo 0.00000000
## Q118233.fctrYes 0.00000000
## Q115602.fctrNo 7.49895999
## Q116953.fctrYes 16.38551337
## Income.fctr.Q 5.01776984
## Q111848.fctrNo 20.14164588
## Q124742.fctrNo 0.00000000
## Q105655.fctrYes 0.00000000
## Q106388.fctrYes 19.44404023
## Q111580.fctrSupportive 3.08478749
## Q124122.fctrYes 2.30458741
## Q115777.fctrStart 11.56229740
## Q114517.fctrYes 0.00000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 1.47081274
## Q105840.fctrNo 6.75070028
## Q108343.fctrNo 0.00000000
## Income.fctr^4 19.96596182
## Q106993.fctrYes 0.00000000
## Q121699.fctrNo 8.90787235
## Edn.fctr^6 14.02376046
## Q109244.fctrNA:.clusterid.fctr1 0.00000000
## Q116448.fctrNo 17.38150488
## Q102687.fctrNo 6.38732137
## Income.fctr.L 0.00000000
## Q112512.fctrYes 1.54061708
## Q108343.fctrYes 5.38349362
## Q109367.fctrNo 30.76271919
## Q102906.fctrNo 0.00000000
## Q114152.fctrYes 2.97986267
## Q109244.fctrNA:.clusterid.fctr4 0.00000000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.00000000
## Hhold.fctrMKn 3.96941185
## Income.fctr^5 0.00000000
## Q100010.fctrYes 0.00000000
## Q100562.fctrYes 0.00000000
## Q100680.fctrYes 0.00000000
## Q102289.fctrNo 0.01831279
## Q102906.fctrYes 5.47225482
## Q103293.fctrYes 0.00000000
## Q104996.fctrYes 9.06503107
## Q105655.fctrNo 0.00000000
## Q106272.fctrNo 16.77475600
## Q106997.fctrGr 2.91187343
## Q106997.fctrYy 0.00000000
## Q107869.fctrNo 0.00000000
## Q108617.fctrYes 0.00000000
## Q108754.fctrNo 0.00000000
## Q108754.fctrYes 0.00000000
## Q108855.fctrYes! 0.00000000
## Q109244.fctrNo:.clusterid.fctr1 0.00000000
## Q109244.fctrNo:.clusterid.fctr2 0.00000000
## Q109244.fctrNo:.clusterid.fctr3 0.00000000
## Q109244.fctrNo:.clusterid.fctr4 0.00000000
## Q109244.fctrYes:.clusterid.fctr1 0.00000000
## Q109244.fctrYes:.clusterid.fctr2 0.00000000
## Q109244.fctrYes:.clusterid.fctr3 0.00000000
## Q109244.fctrYes:.clusterid.fctr4 0.00000000
## Q111220.fctrNo 0.00000000
## Q111220.fctrYes 0.00000000
## Q112478.fctrYes 0.00000000
## Q112512.fctrNo 24.13237615
## Q113584.fctrTechnology 0.00000000
## Q113992.fctrNo 0.12317152
## Q114748.fctrNo 4.44030358
## Q114748.fctrYes 0.00000000
## Q115602.fctrYes 0.00000000
## Q115610.fctrNo 21.26751769
## Q115610.fctrYes 1.87689570
## Q116448.fctrYes 3.16653709
## Q117193.fctrStandard hours 2.69447397
## Q118117.fctrNo 0.00000000
## Q119650.fctrGiving 7.93463365
## Q120014.fctrYes 5.26971744
## Q120472.fctrArt 0.00000000
## Q120978.fctrNo 0.00000000
## Q121011.fctrYes 0.00000000
## Q121700.fctrNo 1.26704991
## Q122769.fctrYes 0.00000000
## Q123464.fctrNo 0.00000000
## Q123621.fctrYes 0.00000000
## Q96024.fctrYes 10.51724456
## Q98078.fctrYes 0.00000000
## Q98869.fctrYes 0.83171509
## Q99716.fctrYes 0.00000000
## Q99982.fctrCheck! 9.99813228
## Q99982.fctrNope 0.00000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 4.78865841
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.00000000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.26203741
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.22402287
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.00000000
## YOB.Age.fctrNA:YOB.Age.dff 0.00000000
## YOB.Age.fctr^5 3.32559612
## YOB.Age.fctr^6 0.00000000
## YOB.Age.fctr^7 15.50614515
## Final.All.X...glmnet.imp imp
## Q101163.fctrMom 1.000000e+02 1.000000e+02
## Q98197.fctrNo 9.845073e+01 9.845073e+01
## Hhold.fctrPKy 9.373465e+01 9.373465e+01
## Q98059.fctrOnly-child 9.370186e+01 9.370186e+01
## Q113181.fctrYes 8.684490e+01 8.684490e+01
## Q101596.fctrYes 8.638678e+01 8.638678e+01
## Q108856.fctrSocialize 8.430518e+01 8.430518e+01
## Q100562.fctrNo 8.265561e+01 8.265561e+01
## Q115611.fctrYes 8.095599e+01 8.095599e+01
## Q108950.fctrRisk-friendly 8.007861e+01 8.007861e+01
## Q98869.fctrNo 7.942760e+01 7.942760e+01
## Q108342.fctrOnline 7.733040e+01 7.733040e+01
## Q102674.fctrYes 7.160218e+01 7.160218e+01
## Q116601.fctrNo 7.067989e+01 7.067989e+01
## Q99480.fctrYes 6.345133e+01 6.345133e+01
## Q100689.fctrYes 6.332185e+01 6.332185e+01
## Q113583.fctrTunes 6.302831e+01 6.302831e+01
## Q120650.fctrNo 6.302397e+01 6.302397e+01
## YOB.Age.fctr.L 5.833462e+01 5.833462e+01
## Q115390.fctrNo 5.759981e+01 5.759981e+01
## Q123464.fctrYes 5.652981e+01 5.652981e+01
## Hhold.fctrPKn 5.608462e+01 5.608462e+01
## YOB.Age.fctr.Q 5.359071e+01 5.359071e+01
## Q108855.fctrUmm... 5.196360e+01 5.196360e+01
## Q112270.fctrYes 5.190008e+01 5.190008e+01
## Q106389.fctrNo 5.103464e+01 5.103464e+01
## Q101162.fctrOptimist 4.949061e+01 4.949061e+01
## Income.fctr.C 4.869976e+01 4.869976e+01
## Q116441.fctrYes 4.859949e+01 4.859949e+01
## Edn.fctr^4 4.768903e+01 4.768903e+01
## Q115611.fctrNo 4.658572e+01 4.658572e+01
## Gender.fctrM 4.515375e+01 4.515375e+01
## Q112478.fctrNo 4.507562e+01 4.507562e+01
## Q119851.fctrNo 4.407891e+01 4.407891e+01
## Q106389.fctrYes 4.355789e+01 4.355789e+01
## Q122771.fctrPt 4.129262e+01 4.129262e+01
## Q100010.fctrNo 4.031552e+01 4.031552e+01
## Q115195.fctrNo 3.996006e+01 3.996006e+01
## Edn.fctr^7 3.900826e+01 3.900826e+01
## Q116601.fctrYes 3.841897e+01 3.841897e+01
## Edn.fctr.Q 3.834841e+01 3.834841e+01
## Q122120.fctrYes 3.794797e+01 3.794797e+01
## Q118232.fctrPr 3.766346e+01 3.766346e+01
## Q110740.fctrPC 3.538847e+01 3.538847e+01
## Q108617.fctrNo 3.517782e+01 3.517782e+01
## Q120194.fctrStudy first 3.484334e+01 3.484334e+01
## Q120650.fctrYes 3.467375e+01 3.467375e+01
## Q116197.fctrP.M. 3.462373e+01 3.462373e+01
## Q99716.fctrNo 3.447471e+01 3.447471e+01
## Q107491.fctrNo 3.412454e+01 3.412454e+01
## Q104996.fctrNo 3.393338e+01 3.393338e+01
## Q109367.fctrYes 3.356524e+01 3.356524e+01
## Q116441.fctrNo 3.311260e+01 3.311260e+01
## Q98578.fctrNo 3.303303e+01 3.303303e+01
## Q118232.fctrId 3.271330e+01 3.271330e+01
## Q120379.fctrYes 3.190047e+01 3.190047e+01
## Q121699.fctrYes 3.143948e+01 3.143948e+01
## Q98197.fctrYes 3.119298e+01 3.119298e+01
## Q112270.fctrNo 3.087819e+01 3.087819e+01
## Q113992.fctrYes 3.036106e+01 3.036106e+01
## Q118237.fctrYes 3.009601e+01 3.009601e+01
## Q115777.fctrEnd 2.963343e+01 2.963343e+01
## Q121700.fctrYes 2.779926e+01 2.779926e+01
## Q102674.fctrNo 2.672827e+01 2.672827e+01
## Q120472.fctrScience 2.644680e+01 2.644680e+01
## Q117186.fctrCool headed 2.624983e+01 2.624983e+01
## Q101596.fctrNo 2.457771e+01 2.457771e+01
## Q115899.fctrCs 2.453569e+01 2.453569e+01
## Q106388.fctrNo 2.410791e+01 2.410791e+01
## Q122770.fctrNo 2.409050e+01 2.409050e+01
## Q107491.fctrYes 2.357096e+01 2.357096e+01
## Q124122.fctrNo 2.354257e+01 2.354257e+01
## Q118892.fctrYes 2.337477e+01 2.337477e+01
## Q101163.fctrDad 2.333790e+01 2.333790e+01
## Q99480.fctrNo 2.309701e+01 2.309701e+01
## Q114386.fctrTMI 2.281308e+01 2.281308e+01
## Q102089.fctrOwn 2.259473e+01 2.259473e+01
## Q109244.fctrNA:.clusterid.fctr2 2.255599e+01 2.255599e+01
## Q118233.fctrNo 2.237498e+01 2.237498e+01
## Q120379.fctrNo 2.237158e+01 2.237158e+01
## Q102089.fctrRent 2.196460e+01 2.196460e+01
## Q115195.fctrYes 2.162368e+01 2.162368e+01
## Q123621.fctrNo 2.153856e+01 2.153856e+01
## Q113181.fctrNo 2.073920e+01 2.073920e+01
## Q100689.fctrNo 2.049609e+01 2.049609e+01
## Q116797.fctrNo 2.047821e+01 2.047821e+01
## Q110740.fctrMac 2.033579e+01 2.033579e+01
## Q121011.fctrNo 1.961604e+01 1.961604e+01
## Q99581.fctrYes 1.957552e+01 1.957552e+01
## Q98578.fctrYes 1.947814e+01 1.947814e+01
## YOB.Age.fctr(35,40]:YOB.Age.dff 1.850769e+01 1.850769e+01
## Hhold.fctrSKn 1.837278e+01 1.837278e+01
## Q119851.fctrYes 1.831841e+01 1.831841e+01
## Q111848.fctrYes 1.819680e+01 1.819680e+01
## Q100680.fctrNo 1.804439e+01 1.804439e+01
## Q114517.fctrNo 1.758490e+01 1.758490e+01
## Hhold.fctrSKy 1.709075e+01 1.709075e+01
## YOB.Age.fctr^8 1.699004e+01 1.699004e+01
## Q106272.fctrYes 1.681861e+01 1.681861e+01
## YOB.Age.fctr^4 1.670103e+01 1.670103e+01
## Q114961.fctrNo 1.636166e+01 1.636166e+01
## Q106993.fctrNo 1.604187e+01 1.604187e+01
## Q116881.fctrRight 1.599978e+01 1.599978e+01
## Q108342.fctrIn-person 1.584587e+01 1.584587e+01
## Q120012.fctrNo 1.579992e+01 1.579992e+01
## Q122771.fctrPc 1.550507e+01 1.550507e+01
## Q114961.fctrYes 1.490786e+01 1.490786e+01
## Q114152.fctrNo 1.451741e+01 1.451741e+01
## Q117186.fctrHot headed 1.447748e+01 1.447748e+01
## Q115899.fctrMe 1.373929e+01 1.373929e+01
## Q114386.fctrMysterious 1.359821e+01 1.359821e+01
## Q109244.fctrNA:.clusterid.fctr3 1.323519e+01 1.323519e+01
## Q118892.fctrNo 1.260067e+01 1.260067e+01
## Q108856.fctrSpace 1.246620e+01 1.246620e+01
## Q106042.fctrNo 1.234176e+01 1.234176e+01
## Q120014.fctrNo 1.232189e+01 1.232189e+01
## Q99581.fctrNo 1.214452e+01 1.214452e+01
## Hhold.fctrMKy 1.208334e+01 1.208334e+01
## Q107869.fctrYes 1.163957e+01 1.163957e+01
## Q120194.fctrTry first 1.156545e+01 1.156545e+01
## Gender.fctrF 1.152499e+01 1.152499e+01
## Q118237.fctrNo 1.131810e+01 1.131810e+01
## Q116881.fctrHappy 1.120330e+01 1.120330e+01
## Q119334.fctrYes 1.106326e+01 1.106326e+01
## Q98078.fctrNo 1.098837e+01 1.098837e+01
## Q115390.fctrYes 1.064503e+01 1.064503e+01
## Q101162.fctrPessimist 1.045688e+01 1.045688e+01
## Q116953.fctrNo 1.016851e+01 1.016851e+01
## Q116197.fctrA.M. 9.935640e+00 9.935640e+00
## Q106042.fctrYes 9.925344e+00 9.925344e+00
## Q118117.fctrYes 9.899272e+00 9.899272e+00
## Q98059.fctrYes 9.739722e+00 9.739722e+00
## Q122769.fctrNo 9.573748e+00 9.573748e+00
## .rnorm 9.542615e+00 9.542615e+00
## Income.fctr^6 9.461021e+00 9.461021e+00
## Q102687.fctrYes 9.207218e+00 9.207218e+00
## YOB.Age.fctr.C 9.106516e+00 9.106516e+00
## Edn.fctr^5 8.964927e+00 8.964927e+00
## Q122770.fctrYes 8.798447e+00 8.798447e+00
## Q105840.fctrYes 8.791584e+00 8.791584e+00
## Q108950.fctrCautious 8.667295e+00 8.667295e+00
## Edn.fctr.L 8.241107e+00 8.241107e+00
## Q120012.fctrYes 7.322620e+00 7.322620e+00
## Q119334.fctrNo 7.072805e+00 7.072805e+00
## Q103293.fctrNo 7.018926e+00 7.018926e+00
## Edn.fctr.C 6.828947e+00 6.828947e+00
## Q113583.fctrTalk 6.575192e+00 6.575192e+00
## Q120978.fctrYes 6.534009e+00 6.534009e+00
## Q113584.fctrPeople 6.165363e+00 6.165363e+00
## Q111580.fctrDemanding 5.951465e+00 5.951465e+00
## Q119650.fctrReceiving 5.945490e+00 5.945490e+00
## Q102289.fctrYes 5.901717e+00 5.901717e+00
## Q116797.fctrYes 5.841529e+00 5.841529e+00
## Q124742.fctrYes 4.864655e+00 4.864655e+00
## Q117193.fctrOdd hours 4.807532e+00 4.807532e+00
## Q96024.fctrNo 4.797362e+00 4.797362e+00
## Q122120.fctrNo 4.675901e+00 4.675901e+00
## Q118233.fctrYes 4.637856e+00 4.637856e+00
## Q115602.fctrNo 4.385208e+00 4.385208e+00
## Q116953.fctrYes 4.120263e+00 4.120263e+00
## Income.fctr.Q 4.082232e+00 4.082232e+00
## Q111848.fctrNo 3.968675e+00 3.968675e+00
## Q124742.fctrNo 3.611213e+00 3.611213e+00
## Q105655.fctrYes 3.473468e+00 3.473468e+00
## Q106388.fctrYes 3.279316e+00 3.279316e+00
## Q111580.fctrSupportive 3.197008e+00 3.197008e+00
## Q124122.fctrYes 2.915530e+00 2.915530e+00
## Q115777.fctrStart 2.825172e+00 2.825172e+00
## Q114517.fctrYes 2.782313e+00 2.782313e+00
## YOB.Age.fctr(15,20]:YOB.Age.dff 2.280279e+00 2.280279e+00
## Q105840.fctrNo 2.081460e+00 2.081460e+00
## Q108343.fctrNo 1.659316e+00 1.659316e+00
## Income.fctr^4 1.647360e+00 1.647360e+00
## Q106993.fctrYes 1.626785e+00 1.626785e+00
## Q121699.fctrNo 1.615288e+00 1.615288e+00
## Edn.fctr^6 1.351951e+00 1.351951e+00
## Q109244.fctrNA:.clusterid.fctr1 1.174824e+00 1.174824e+00
## Q116448.fctrNo 1.129749e+00 1.129749e+00
## Q102687.fctrNo 9.481816e-01 9.481816e-01
## Income.fctr.L 9.387771e-01 9.387771e-01
## Q112512.fctrYes 6.092220e-01 6.092220e-01
## Q108343.fctrYes 3.829357e-01 3.829357e-01
## Q109367.fctrNo 3.277773e-01 3.277773e-01
## Q102906.fctrNo 1.363832e-01 1.363832e-01
## Q114152.fctrYes 1.086266e-01 1.086266e-01
## Q109244.fctrNA:.clusterid.fctr4 2.299094e-02 2.299094e-02
## YOB.Age.fctr(40,50]:YOB.Age.dff 1.668896e-03 1.668896e-03
## Hhold.fctrMKn 0.000000e+00 0.000000e+00
## Income.fctr^5 0.000000e+00 0.000000e+00
## Q100010.fctrYes 0.000000e+00 0.000000e+00
## Q100562.fctrYes 0.000000e+00 0.000000e+00
## Q100680.fctrYes 0.000000e+00 0.000000e+00
## Q102289.fctrNo 0.000000e+00 0.000000e+00
## Q102906.fctrYes 0.000000e+00 0.000000e+00
## Q103293.fctrYes 0.000000e+00 0.000000e+00
## Q104996.fctrYes 0.000000e+00 0.000000e+00
## Q105655.fctrNo 0.000000e+00 0.000000e+00
## Q106272.fctrNo 0.000000e+00 0.000000e+00
## Q106997.fctrGr 0.000000e+00 0.000000e+00
## Q106997.fctrYy 0.000000e+00 0.000000e+00
## Q107869.fctrNo 0.000000e+00 0.000000e+00
## Q108617.fctrYes 0.000000e+00 0.000000e+00
## Q108754.fctrNo 0.000000e+00 0.000000e+00
## Q108754.fctrYes 0.000000e+00 0.000000e+00
## Q108855.fctrYes! 0.000000e+00 0.000000e+00
## Q109244.fctrNo:.clusterid.fctr1 0.000000e+00 0.000000e+00
## Q109244.fctrNo:.clusterid.fctr2 0.000000e+00 0.000000e+00
## Q109244.fctrNo:.clusterid.fctr3 0.000000e+00 0.000000e+00
## Q109244.fctrNo:.clusterid.fctr4 0.000000e+00 0.000000e+00
## Q109244.fctrYes:.clusterid.fctr1 0.000000e+00 0.000000e+00
## Q109244.fctrYes:.clusterid.fctr2 0.000000e+00 0.000000e+00
## Q109244.fctrYes:.clusterid.fctr3 0.000000e+00 0.000000e+00
## Q109244.fctrYes:.clusterid.fctr4 0.000000e+00 0.000000e+00
## Q111220.fctrNo 0.000000e+00 0.000000e+00
## Q111220.fctrYes 0.000000e+00 0.000000e+00
## Q112478.fctrYes 0.000000e+00 0.000000e+00
## Q112512.fctrNo 0.000000e+00 0.000000e+00
## Q113584.fctrTechnology 0.000000e+00 0.000000e+00
## Q113992.fctrNo 0.000000e+00 0.000000e+00
## Q114748.fctrNo 0.000000e+00 0.000000e+00
## Q114748.fctrYes 0.000000e+00 0.000000e+00
## Q115602.fctrYes 0.000000e+00 0.000000e+00
## Q115610.fctrNo 0.000000e+00 0.000000e+00
## Q115610.fctrYes 0.000000e+00 0.000000e+00
## Q116448.fctrYes 0.000000e+00 0.000000e+00
## Q117193.fctrStandard hours 0.000000e+00 0.000000e+00
## Q118117.fctrNo 0.000000e+00 0.000000e+00
## Q119650.fctrGiving 0.000000e+00 0.000000e+00
## Q120014.fctrYes 0.000000e+00 0.000000e+00
## Q120472.fctrArt 0.000000e+00 0.000000e+00
## Q120978.fctrNo 0.000000e+00 0.000000e+00
## Q121011.fctrYes 0.000000e+00 0.000000e+00
## Q121700.fctrNo 0.000000e+00 0.000000e+00
## Q122769.fctrYes 0.000000e+00 0.000000e+00
## Q123464.fctrNo 0.000000e+00 0.000000e+00
## Q123621.fctrYes 0.000000e+00 0.000000e+00
## Q96024.fctrYes 0.000000e+00 0.000000e+00
## Q98078.fctrYes 0.000000e+00 0.000000e+00
## Q98869.fctrYes 0.000000e+00 0.000000e+00
## Q99716.fctrYes 0.000000e+00 0.000000e+00
## Q99982.fctrCheck! 0.000000e+00 0.000000e+00
## Q99982.fctrNope 0.000000e+00 0.000000e+00
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctrNA:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr^5 0.000000e+00 0.000000e+00
## YOB.Age.fctr^6 0.000000e+00 0.000000e+00
## YOB.Age.fctr^7 0.000000e+00 0.000000e+00if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 1264 R 0.05188727
## 2 4660 R 0.08817815
## 3 1461 R 0.09734940
## 4 5551 R 0.08817253
## 5 3380 R 0.16232428
## 6 2591 R 0.13902421
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 D TRUE
## 2 D TRUE
## 3 D TRUE
## 4 D TRUE
## 5 D TRUE
## 6 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.9481127 FALSE
## 2 0.9118219 FALSE
## 3 0.9026506 FALSE
## 4 0.9118275 FALSE
## 5 0.8376757 FALSE
## 6 0.8609758 FALSE
## Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 1 0.06870115 D
## 2 0.07837419 D
## 3 0.08082581 D
## 4 0.11546521 D
## 5 0.12140736 D
## 6 0.15882397 D
## Party.fctr.Final.All.X...glmnet.err
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## Party.fctr.Final.All.X...glmnet.err.abs
## 1 0.9312988
## 2 0.9216258
## 3 0.9191742
## 4 0.8845348
## 5 0.8785926
## 6 0.8411760
## Party.fctr.Final.All.X...glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final.All.X...glmnet.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final.All.X...glmnet.error
## 1 -0.3312988
## 2 -0.3216258
## 3 -0.3191742
## 4 -0.2845348
## 5 -0.2785926
## 6 -0.2411760
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 76 6839 R 0.2917948
## 461 305 D NA
## 541 6823 D NA
## 608 2822 D NA
## 649 6737 D 0.6018561
## 787 2495 D 0.7964733
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 76 D TRUE
## 461 <NA> NA
## 541 <NA> NA
## 608 <NA> NA
## 649 R TRUE
## 787 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 76 0.7082052
## 461 NA
## 541 NA
## 608 NA
## 649 0.6018561
## 787 0.7964733
## Party.fctr.All.X..rcv.glmnet.is.acc
## 76 FALSE
## 461 NA
## 541 NA
## 608 NA
## 649 FALSE
## 787 FALSE
## Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 76 0.3245193 D
## 461 0.4899776 R
## 541 0.5178457 R
## 608 0.5485639 R
## 649 0.5780908 R
## 787 0.7763096 R
## Party.fctr.Final.All.X...glmnet.err
## 76 TRUE
## 461 TRUE
## 541 TRUE
## 608 TRUE
## 649 TRUE
## 787 TRUE
## Party.fctr.Final.All.X...glmnet.err.abs
## 76 0.6754807
## 461 0.4899776
## 541 0.5178457
## 608 0.5485639
## 649 0.5780908
## 787 0.7763096
## Party.fctr.Final.All.X...glmnet.is.acc
## 76 FALSE
## 461 FALSE
## 541 FALSE
## 608 FALSE
## 649 FALSE
## 787 FALSE
## Party.fctr.Final.All.X...glmnet.accurate
## 76 FALSE
## 461 FALSE
## 541 FALSE
## 608 FALSE
## 649 FALSE
## 787 FALSE
## Party.fctr.Final.All.X...glmnet.error
## 76 -0.07548066
## 461 0.08997759
## 541 0.11784572
## 608 0.14856389
## 649 0.17809084
## 787 0.37630964
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 786 1538 D 0.7193312
## 787 2495 D 0.7964733
## 788 485 D 0.8208562
## 789 6053 D NA
## 790 4350 D NA
## 791 3431 D NA
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 786 R TRUE
## 787 R TRUE
## 788 R TRUE
## 789 <NA> NA
## 790 <NA> NA
## 791 <NA> NA
## Party.fctr.All.X..rcv.glmnet.err.abs
## 786 0.7193312
## 787 0.7964733
## 788 0.8208562
## 789 NA
## 790 NA
## 791 NA
## Party.fctr.All.X..rcv.glmnet.is.acc
## 786 FALSE
## 787 FALSE
## 788 FALSE
## 789 NA
## 790 NA
## 791 NA
## Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 786 0.7708571 R
## 787 0.7763096 R
## 788 0.7772199 R
## 789 0.7987214 R
## 790 0.8247472 R
## 791 0.9109257 R
## Party.fctr.Final.All.X...glmnet.err
## 786 TRUE
## 787 TRUE
## 788 TRUE
## 789 TRUE
## 790 TRUE
## 791 TRUE
## Party.fctr.Final.All.X...glmnet.err.abs
## 786 0.7708571
## 787 0.7763096
## 788 0.7772199
## 789 0.7987214
## 790 0.8247472
## 791 0.9109257
## Party.fctr.Final.All.X...glmnet.is.acc
## 786 FALSE
## 787 FALSE
## 788 FALSE
## 789 FALSE
## 790 FALSE
## 791 FALSE
## Party.fctr.Final.All.X...glmnet.accurate
## 786 FALSE
## 787 FALSE
## 788 FALSE
## 789 FALSE
## 790 FALSE
## 791 FALSE
## Party.fctr.Final.All.X...glmnet.error
## 786 0.3708571
## 787 0.3763096
## 788 0.3772199
## 789 0.3987214
## 790 0.4247472
## 791 0.5109257
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "Party.fctr.Final.All.X...glmnet.prob"
## [2] "Party.fctr.Final.All.X...glmnet"
## [3] "Party.fctr.Final.All.X...glmnet.err"
## [4] "Party.fctr.Final.All.X...glmnet.err.abs"
## [5] "Party.fctr.Final.All.X...glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 21 fit.data.training 9 1 1 389.605 396.092
## 22 predict.data.new 10 0 0 396.093 NA
## elapsed
## 21 6.488
## 22 NAStep 10.0: predict data new
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.4
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.4## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## Warning: Removed 547 rows containing missing values (geom_point).
## NULL## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"## [1] "Stacking file Q109244No_AllXpreProc_cnk03_rest_out_fin.csv to prediction outputs..."## [1] 0.4## [1] "glbMdlSelId: All.X##rcv#glmnet"## [1] "glbMdlFinId: Final.All.X###glmnet"## [1] "Cross Validation issues:"
## MFO###myMFO_classfr Random###myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1###glmnet Final.All.X###glmnet
## 0 0## max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glmnet 0.5867580 0.6153233
## Low.cor.X##rcv#glmnet 0.5730594 0.6077246
## Interact.High.cor.Y##rcv#glmnet 0.5662100 0.5851273
## Max.cor.Y.rcv.1X1###glmnet 0.5410959 0.5584215
## Max.cor.Y##rcv#rpart 0.5410959 0.5448590
## Random###myrandom_classfr 0.5365297 0.5042029
## MFO###myMFO_classfr 0.5365297 0.5000000
## Final.All.X###glmnet NA NA
## max.AUCpROC.OOB min.elapsedtime.everything
## All.X##rcv#glmnet 0.5683681 7.303
## Low.cor.X##rcv#glmnet 0.5571848 6.595
## Interact.High.cor.Y##rcv#glmnet 0.5598575 2.492
## Max.cor.Y.rcv.1X1###glmnet 0.5441673 0.710
## Max.cor.Y##rcv#rpart 0.5441673 1.464
## Random###myrandom_classfr 0.5576145 0.381
## MFO###myMFO_classfr 0.5000000 0.485
## Final.All.X###glmnet NA 1.761
## max.Accuracy.fit opt.prob.threshold.fit
## All.X##rcv#glmnet 0.5689194 0.45
## Low.cor.X##rcv#glmnet 0.5631921 0.45
## Interact.High.cor.Y##rcv#glmnet 0.5578465 0.50
## Max.cor.Y.rcv.1X1###glmnet 0.5595647 0.50
## Max.cor.Y##rcv#rpart 0.5595647 0.50
## Random###myrandom_classfr 0.5360825 0.55
## MFO###myMFO_classfr 0.5360825 0.50
## Final.All.X###glmnet 0.6717033 0.50
## opt.prob.threshold.OOB
## All.X##rcv#glmnet 0.40
## Low.cor.X##rcv#glmnet 0.50
## Interact.High.cor.Y##rcv#glmnet 0.50
## Max.cor.Y.rcv.1X1###glmnet 0.50
## Max.cor.Y##rcv#rpart 0.50
## Random###myrandom_classfr 0.55
## MFO###myMFO_classfr 0.50
## Final.All.X###glmnet NA## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference D R
## D 112 123
## R 58 145## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## NA 737.9715 202.5673 944.8894 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## NA 1 1 1 1746 206 341
## .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## NA 438 1171 1013 547 1746 547 2184 0.4624824
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## NA 0.4226641 NA 0.4326417
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 737.9714939 202.5673105 944.8893983 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.0000000 1.0000000 1.0000000 1746.0000000
## .n.New.D .n.New.R .n.OOB .n.Trn.D
## 206.0000000 341.0000000 438.0000000 1171.0000000
## .n.Trn.R .n.Tst .n.fit .n.new
## 1013.0000000 547.0000000 1746.0000000 547.0000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 2184.0000000 0.4624824 0.4226641 NA
## err.abs.trn.mean
## 0.4326417## [1] "Features Importance for selected models:"## All.X..rcv.glmnet.imp
## Hhold.fctrPKy 100.000000
## Q98197.fctrNo 65.048286
## Q101163.fctrMom 58.708899
## Q123464.fctrYes 58.376542
## Q100562.fctrNo 56.991037
## Q108950.fctrRisk-friendly 54.825487
## Q115611.fctrYes 54.361193
## Q113181.fctrYes 53.879551
## Q100689.fctrYes 51.337948
## Q98869.fctrNo 49.757502
## Q101596.fctrYes 48.667778
## Q116601.fctrNo 48.168111
## Q99480.fctrYes 45.370603
## Q120650.fctrNo 45.127038
## Q108856.fctrSocialize 44.049797
## Q102674.fctrYes 42.374043
## Q98059.fctrOnly-child 41.816050
## Q113583.fctrTunes 40.142709
## Q122771.fctrPt 38.593261
## YOB.Age.fctr.L 38.504618
## Q112478.fctrNo 37.763663
## Q115390.fctrNo 35.851334
## Q112270.fctrYes 34.699735
## Q102089.fctrRent 34.328231
## Q120379.fctrYes 33.890156
## Edn.fctr^4 33.474144
## YOB.Age.fctr.Q 32.725231
## Q116441.fctrYes 32.561349
## Q116441.fctrNo 31.961122
## Gender.fctrM 30.991757
## Q109367.fctrNo 30.762719
## Q108855.fctrUmm... 30.651230
## Q101162.fctrPessimist 30.464319
## Q110740.fctrPC 29.995557
## Q115899.fctrCs 29.831009
## Q106389.fctrNo 29.797007
## Q100010.fctrNo 29.534982
## Q106388.fctrNo 28.714152
## Q106389.fctrYes 27.246625
## Q115611.fctrNo 26.632569
## Income.fctr.C 26.585030
## Q108342.fctrOnline 26.519873
## Q104996.fctrNo 25.762556
## Q120194.fctrStudy first 25.250573
## Q102089.fctrOwn 25.196772
## Q122120.fctrYes 25.143277
## Q107491.fctrYes 25.107161
## Q115195.fctrYes 25.008801
## Q108617.fctrNo 24.861481
## Q116797.fctrNo 24.844391
## Edn.fctr.Q 24.488980
## Q112512.fctrNo 24.132376
## Q98197.fctrYes 23.920028
## Q99716.fctrNo 23.154532
## Q118232.fctrPr 23.059123
## Q98578.fctrYes 22.695485
## Q116197.fctrP.M. 21.745735
## Q100680.fctrNo 21.324913
## Q115610.fctrNo 21.267518
## Q119851.fctrNo 20.743225
## Q110740.fctrMac 20.432264
## Q111848.fctrNo 20.141646
## Q98578.fctrNo 20.027792
## Income.fctr^4 19.965962
## Q115777.fctrEnd 19.742700
## Q114386.fctrMysterious 19.450401
## Q106388.fctrYes 19.444040
## Q116601.fctrYes 19.116540
## Q99480.fctrNo 19.027483
## Q117186.fctrCool headed 18.799709
## Q102674.fctrNo 18.686343
## Q115195.fctrNo 18.538604
## Q120472.fctrScience 18.463879
## Q120014.fctrNo 18.255955
## Q116448.fctrNo 17.381505
## Q109367.fctrYes 17.309567
## Edn.fctr^5 17.188696
## Q102289.fctrYes 17.162123
## Q114961.fctrYes 16.792500
## Q106272.fctrNo 16.774756
## Q118117.fctrYes 16.713073
## Q118892.fctrYes 16.652916
## Q116953.fctrYes 16.385513
## Q121700.fctrYes 16.103522
## Hhold.fctrPKn 15.645196
## YOB.Age.fctr^7 15.506145
## Q120978.fctrYes 15.303583
## Q120650.fctrYes 15.292652
## Q107491.fctrNo 15.269665
## Hhold.fctrMKy 14.861675
## Q101162.fctrOptimist 14.793446
## Q121699.fctrYes 14.694718
## Q106042.fctrNo 14.316775
## Q121011.fctrNo 14.277880
## Edn.fctr^6 14.023760
## Q123621.fctrNo 13.994117
## Q101163.fctrDad 13.879981
## YOB.Age.fctr.C 13.676680
## Q120012.fctrNo 13.534430
## Edn.fctr^7 13.334549
## Q118232.fctrId 12.824690
## Q101596.fctrNo 12.592090
## Hhold.fctrSKn 12.588092
## Q106993.fctrNo 12.466488
## Income.fctr^6 12.207976
## Q106272.fctrYes 12.095670
## Q114386.fctrTMI 12.090392
## Q116953.fctrNo 12.044159
## Q116797.fctrYes 11.902041
## Q115777.fctrStart 11.562297
## Q120379.fctrNo 11.455087
## Q116881.fctrRight 11.317870
## Q117186.fctrHot headed 11.149297
## Q114517.fctrNo 11.124766
## Q108856.fctrSpace 10.950168
## Q96024.fctrYes 10.517245
## Q113181.fctrNo 10.216551
## YOB.Age.fctr^8 10.193243
## Q112270.fctrNo 10.169781
## Q118233.fctrNo 9.987533
## Q108342.fctrIn-person 9.872076
## Q119334.fctrYes 8.319375
## Q111848.fctrYes 8.042110
## Q124122.fctrNo 7.977838
## Q99581.fctrYes 7.636125
## Q122770.fctrNo 7.539847
## Q119851.fctrYes 7.498226
## Q100689.fctrNo 7.409099
## Q109244.fctrNA:.clusterid.fctr3 7.406972
## YOB.Age.fctr^4 7.329632
## Gender.fctrF 6.851830
## YOB.Age.fctr(35,40]:YOB.Age.dff 6.719081
## Q115899.fctrMe 6.439028
## Q107869.fctrYes 6.347354
## Q113992.fctrYes 5.761898
## Q114961.fctrNo 5.642044
## Q98078.fctrNo 3.228905
## Q118892.fctrNo 3.161839
## Q118237.fctrNo 2.919547
## Q122771.fctrPc 2.845281
## Q109244.fctrNA:.clusterid.fctr2 2.093614
## Q118237.fctrYes 0.000000
## Hhold.fctrSKy 0.000000
## Q114152.fctrNo 0.000000
## Q99581.fctrNo 0.000000
## Q120194.fctrTry first 0.000000
## Q116881.fctrHappy 0.000000
## Q115390.fctrYes 0.000000
## Final.All.X...glmnet.imp
## Hhold.fctrPKy 93.7346540
## Q98197.fctrNo 98.4507341
## Q101163.fctrMom 100.0000000
## Q123464.fctrYes 56.5298106
## Q100562.fctrNo 82.6556096
## Q108950.fctrRisk-friendly 80.0786117
## Q115611.fctrYes 80.9559941
## Q113181.fctrYes 86.8448993
## Q100689.fctrYes 63.3218547
## Q98869.fctrNo 79.4276036
## Q101596.fctrYes 86.3867841
## Q116601.fctrNo 70.6798922
## Q99480.fctrYes 63.4513280
## Q120650.fctrNo 63.0239655
## Q108856.fctrSocialize 84.3051782
## Q102674.fctrYes 71.6021843
## Q98059.fctrOnly-child 93.7018588
## Q113583.fctrTunes 63.0283055
## Q122771.fctrPt 41.2926240
## YOB.Age.fctr.L 58.3346195
## Q112478.fctrNo 45.0756213
## Q115390.fctrNo 57.5998105
## Q112270.fctrYes 51.9000844
## Q102089.fctrRent 21.9645961
## Q120379.fctrYes 31.9004682
## Edn.fctr^4 47.6890300
## YOB.Age.fctr.Q 53.5907116
## Q116441.fctrYes 48.5994944
## Q116441.fctrNo 33.1126008
## Gender.fctrM 45.1537452
## Q109367.fctrNo 0.3277773
## Q108855.fctrUmm... 51.9636049
## Q101162.fctrPessimist 10.4568764
## Q110740.fctrPC 35.3884747
## Q115899.fctrCs 24.5356857
## Q106389.fctrNo 51.0346434
## Q100010.fctrNo 40.3155171
## Q106388.fctrNo 24.1079058
## Q106389.fctrYes 43.5578863
## Q115611.fctrNo 46.5857230
## Income.fctr.C 48.6997550
## Q108342.fctrOnline 77.3303958
## Q104996.fctrNo 33.9333750
## Q120194.fctrStudy first 34.8433397
## Q102089.fctrOwn 22.5947270
## Q122120.fctrYes 37.9479679
## Q107491.fctrYes 23.5709595
## Q115195.fctrYes 21.6236755
## Q108617.fctrNo 35.1778217
## Q116797.fctrNo 20.4782131
## Edn.fctr.Q 38.3484122
## Q112512.fctrNo 0.0000000
## Q98197.fctrYes 31.1929759
## Q99716.fctrNo 34.4747068
## Q118232.fctrPr 37.6634569
## Q98578.fctrYes 19.4781361
## Q116197.fctrP.M. 34.6237317
## Q100680.fctrNo 18.0443918
## Q115610.fctrNo 0.0000000
## Q119851.fctrNo 44.0789092
## Q110740.fctrMac 20.3357876
## Q111848.fctrNo 3.9686750
## Q98578.fctrNo 33.0330291
## Income.fctr^4 1.6473598
## Q115777.fctrEnd 29.6334344
## Q114386.fctrMysterious 13.5982125
## Q106388.fctrYes 3.2793165
## Q116601.fctrYes 38.4189710
## Q99480.fctrNo 23.0970147
## Q117186.fctrCool headed 26.2498303
## Q102674.fctrNo 26.7282724
## Q115195.fctrNo 39.9600562
## Q120472.fctrScience 26.4468022
## Q120014.fctrNo 12.3218903
## Q116448.fctrNo 1.1297491
## Q109367.fctrYes 33.5652362
## Edn.fctr^5 8.9649272
## Q102289.fctrYes 5.9017171
## Q114961.fctrYes 14.9078644
## Q106272.fctrNo 0.0000000
## Q118117.fctrYes 9.8992717
## Q118892.fctrYes 23.3747671
## Q116953.fctrYes 4.1202631
## Q121700.fctrYes 27.7992573
## Hhold.fctrPKn 56.0846195
## YOB.Age.fctr^7 0.0000000
## Q120978.fctrYes 6.5340086
## Q120650.fctrYes 34.6737468
## Q107491.fctrNo 34.1245353
## Hhold.fctrMKy 12.0833409
## Q101162.fctrOptimist 49.4906098
## Q121699.fctrYes 31.4394825
## Q106042.fctrNo 12.3417586
## Q121011.fctrNo 19.6160444
## Edn.fctr^6 1.3519514
## Q123621.fctrNo 21.5385623
## Q101163.fctrDad 23.3378950
## YOB.Age.fctr.C 9.1065163
## Q120012.fctrNo 15.7999237
## Edn.fctr^7 39.0082599
## Q118232.fctrId 32.7133016
## Q101596.fctrNo 24.5777070
## Hhold.fctrSKn 18.3727801
## Q106993.fctrNo 16.0418684
## Income.fctr^6 9.4610206
## Q106272.fctrYes 16.8186069
## Q114386.fctrTMI 22.8130795
## Q116953.fctrNo 10.1685057
## Q116797.fctrYes 5.8415292
## Q115777.fctrStart 2.8251719
## Q120379.fctrNo 22.3715765
## Q116881.fctrRight 15.9997800
## Q117186.fctrHot headed 14.4774825
## Q114517.fctrNo 17.5848980
## Q108856.fctrSpace 12.4662045
## Q96024.fctrYes 0.0000000
## Q113181.fctrNo 20.7391979
## YOB.Age.fctr^8 16.9900374
## Q112270.fctrNo 30.8781884
## Q118233.fctrNo 22.3749814
## Q108342.fctrIn-person 15.8458733
## Q119334.fctrYes 11.0632564
## Q111848.fctrYes 18.1967973
## Q124122.fctrNo 23.5425712
## Q99581.fctrYes 19.5755240
## Q122770.fctrNo 24.0905001
## Q119851.fctrYes 18.3184093
## Q100689.fctrNo 20.4960918
## Q109244.fctrNA:.clusterid.fctr3 13.2351884
## YOB.Age.fctr^4 16.7010342
## Gender.fctrF 11.5249916
## YOB.Age.fctr(35,40]:YOB.Age.dff 18.5076853
## Q115899.fctrMe 13.7392945
## Q107869.fctrYes 11.6395691
## Q113992.fctrYes 30.3610613
## Q114961.fctrNo 16.3616600
## Q98078.fctrNo 10.9883700
## Q118892.fctrNo 12.6006743
## Q118237.fctrNo 11.3180976
## Q122771.fctrPc 15.5050655
## Q109244.fctrNA:.clusterid.fctr2 22.5559912
## Q118237.fctrYes 30.0960106
## Hhold.fctrSKy 17.0907466
## Q114152.fctrNo 14.5174069
## Q99581.fctrNo 12.1445170
## Q120194.fctrTry first 11.5654460
## Q116881.fctrHappy 11.2033020
## Q115390.fctrYes 10.6450327## [1] "glbObsNew prediction stats:"##
## D R
## 206 341## label step_major step_minor label_minor bgn end
## 22 predict.data.new 10 0 0 396.093 406.979
## 23 display.session.info 11 0 0 406.980 NA
## elapsed
## 22 10.887
## 23 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 2 inspect.data 2 0 0 13.274
## 13 cluster.data 5 0 0 187.380
## 16 fit.models 8 0 0 291.196
## 14 partition.data.training 6 0 0 245.044
## 3 scrub.data 2 1 1 150.665
## 17 fit.models 8 1 1 347.340
## 22 predict.data.new 10 0 0 396.093
## 20 fit.data.training 9 0 0 380.781
## 1 import.data 1 0 0 5.750
## 18 fit.models 8 2 2 370.100
## 21 fit.data.training 9 1 1 389.605
## 19 fit.models 8 3 3 377.582
## 15 select.features 7 0 0 288.391
## 11 extract.features.end 3 6 6 185.865
## 12 manage.missing.data 4 0 0 186.754
## 9 extract.features.text 3 4 4 185.724
## 10 extract.features.string 3 5 5 185.795
## 7 extract.features.image 3 2 2 185.637
## 4 transform.data 2 2 2 185.535
## 6 extract.features.datetime 3 1 1 185.597
## 8 extract.features.price 3 3 3 185.689
## 5 extract.features 3 0 0 185.577
## end elapsed duration
## 2 150.664 137.391 137.390
## 13 245.043 57.663 57.663
## 16 347.339 56.143 56.143
## 14 288.391 43.347 43.347
## 3 185.534 34.870 34.869
## 17 370.099 22.760 22.759
## 22 406.979 10.887 10.886
## 20 389.605 8.824 8.824
## 1 13.273 7.524 7.523
## 18 377.582 7.482 7.482
## 21 396.092 6.488 6.487
## 19 380.781 3.199 3.199
## 15 291.195 2.804 2.804
## 11 186.754 0.889 0.889
## 12 187.379 0.626 0.625
## 9 185.794 0.070 0.070
## 10 185.864 0.070 0.069
## 7 185.689 0.052 0.052
## 4 185.577 0.042 0.042
## 6 185.636 0.039 0.039
## 8 185.723 0.035 0.034
## 5 185.596 0.019 0.019
## [1] "Total Elapsed Time: 406.979 secs"